{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>id</th>\n",
       "      <th>player_1_model</th>\n",
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       "      <th>player_1_temperature</th>\n",
       "      <th>player_2_model</th>\n",
       "      <th>player_2_robot_type</th>\n",
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       "      <th>player_2_won</th>\n",
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      "text/plain": [
       "                 id                                     player_1_model  \\\n",
       "0    20241118172723                               openai:gpt-4o:vision   \n",
       "1    20241118172734                               openai:gpt-4o:vision   \n",
       "2    20241118172734                               openai:gpt-4o:vision   \n",
       "3    20241118172741                               openai:gpt-4o:vision   \n",
       "4    20241118172905                               openai:gpt-4o:vision   \n",
       "..              ...                                                ...   \n",
       "540  20241120082323          anthropic:claude-3-sonnet-20240229:vision   \n",
       "541  20241120090835                  mistral:pixtral-large-latest:text   \n",
       "542  20241120090843            anthropic:claude-3-sonnet-20240229:text   \n",
       "543  20241120094109  together:meta-llama/Llama-3.2-90B-Vision-Instr...   \n",
       "544  20241120100230          anthropic:claude-3-sonnet-20240229:vision   \n",
       "\n",
       "    player_1_robot_type  player_1_temperature  \\\n",
       "0                vision                   0.7   \n",
       "1                vision                   0.7   \n",
       "2                vision                   0.7   \n",
       "3                vision                   0.7   \n",
       "4                vision                   0.7   \n",
       "..                  ...                   ...   \n",
       "540              vision                   0.7   \n",
       "541                text                   0.7   \n",
       "542                text                   0.7   \n",
       "543                text                   0.7   \n",
       "544              vision                   0.7   \n",
       "\n",
       "                          player_2_model player_2_robot_type  \\\n",
       "0                   openai:gpt-4o:vision              vision   \n",
       "1                   openai:gpt-4o:vision              vision   \n",
       "2                   openai:gpt-4o:vision              vision   \n",
       "3                   openai:gpt-4o:vision              vision   \n",
       "4          mistral:pixtral-12b-2409:text                text   \n",
       "..                                   ...                 ...   \n",
       "540            openai:gpt-4o-mini:vision              vision   \n",
       "541                   openai:gpt-4o:text                text   \n",
       "542    mistral:pixtral-large-latest:text                text   \n",
       "543                 openai:gpt-4o:vision              vision   \n",
       "544  mistral:pixtral-large-latest:vision              vision   \n",
       "\n",
       "     player_2_temperature  player_1_won  player_2_won  \n",
       "0                     0.7         False          True  \n",
       "1                     0.7         False          True  \n",
       "2                     0.7         False          True  \n",
       "3                     0.7         False          True  \n",
       "4                     0.7         False          True  \n",
       "..                    ...           ...           ...  \n",
       "540                   0.7         False          True  \n",
       "541                   0.7         False          True  \n",
       "542                   0.7         False          True  \n",
       "543                   0.7         False          True  \n",
       "544                   0.7         False          True  \n",
       "\n",
       "[545 rows x 9 columns]"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"../results.csv\")\n",
    "# strip column names\n",
    "df.columns = [\n",
    "    col.strip() for col in df.columns\n",
    "]\n",
    "\n",
    "# For the same model, we want to split vision version from the text ones\n",
    "df[\"player_1_model\"] += \":\" + df[\"player_1_robot_type\"]\n",
    "df[\"player_2_model\"] += \":\" + df[\"player_2_robot_type\"]\n",
    "\n",
    "df[\"player_1_won\"] = df[\"player_1_won\"] == \"True\"\n",
    "df[\"player_2_won\"] = ~df[\"player_1_won\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_1_won\n",
       "False    545\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"player_1_won\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>games_won</th>\n",
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       "      <th>2</th>\n",
       "      <td>anthropic:claude-3-sonnet-20240229:vision</td>\n",
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       "      <th>6</th>\n",
       "      <td>anthropic:claude-3-haiku-20240307:vision</td>\n",
       "      <td>34</td>\n",
       "      <td>86</td>\n",
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       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>anthropic:claude-3-haiku-20240307:text</td>\n",
       "      <td>18</td>\n",
       "      <td>61</td>\n",
       "      <td>0.295082</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>together:meta-llama/Llama-3.2-90B-Vision-Instr...</td>\n",
       "      <td>9</td>\n",
       "      <td>62</td>\n",
       "      <td>0.145161</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>16</td>\n",
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      ],
      "text/plain": [
       "                                                model  games_won  \\\n",
       "12                                 openai:gpt-4o:text         70   \n",
       "4                           openai:gpt-4o-mini:vision         59   \n",
       "11                            openai:gpt-4o-mini:text         59   \n",
       "13                    mistral:pixtral-12b-2409:vision         46   \n",
       "5                 mistral:pixtral-large-latest:vision         45   \n",
       "10                      mistral:pixtral-12b-2409:text         52   \n",
       "0   together:meta-llama/Llama-3.2-90B-Vision-Instr...          8   \n",
       "9                                openai:gpt-4o:vision         43   \n",
       "2           anthropic:claude-3-sonnet-20240229:vision         23   \n",
       "6            anthropic:claude-3-haiku-20240307:vision         34   \n",
       "3                   mistral:pixtral-large-latest:text         25   \n",
       "7              anthropic:claude-3-haiku-20240307:text         18   \n",
       "1   together:meta-llama/Llama-3.2-90B-Vision-Instr...          9   \n",
       "8             anthropic:claude-3-sonnet-20240229:text         16   \n",
       "\n",
       "    games_played  win_rate  \n",
       "12            70  1.000000  \n",
       "4             63  0.936508  \n",
       "11            77  0.766234  \n",
       "13            63  0.730159  \n",
       "5             70  0.642857  \n",
       "10            91  0.571429  \n",
       "0             15  0.533333  \n",
       "9            108  0.398148  \n",
       "2             58  0.396552  \n",
       "6             86  0.395349  \n",
       "3             70  0.357143  \n",
       "7             61  0.295082  \n",
       "1             62  0.145161  \n",
       "8            120  0.133333  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the number of wins for each model \n",
    "\n",
    "unique_models = df[\"player_1_model\"].unique().tolist()\n",
    "unique_models += df[\"player_2_model\"].unique().tolist()\n",
    "unique_models = list(set(unique_models))\n",
    "\n",
    "# For each model, compute the number of wins (player_1 won if it's player 1), the number of games played and the win rate\n",
    "results = []\n",
    "for model in unique_models:\n",
    "    games_played = 0\n",
    "    wins = 0\n",
    "    for i, row in df.iterrows():\n",
    "        if row[\"player_1_model\"] == model and row[\"player_2_model\"] != model:\n",
    "            games_played += 1\n",
    "            if row[\"player_1_won\"]:\n",
    "                wins += 1\n",
    "            continue\n",
    "        if row[\"player_2_model\"] == model and row[\"player_1_model\"] != model:\n",
    "            games_played += 1\n",
    "            if row[\"player_2_won\"]:\n",
    "                wins += 1\n",
    "            continue\n",
    "    win_rate = wins / games_played\n",
    "    results.append({\n",
    "        \"model\": model,\n",
    "        \"games_won\": wins,\n",
    "        \"games_played\": games_played,\n",
    "        \"win_rate\": win_rate\n",
    "    })\n",
    "\n",
    "# Cast to df \n",
    "results_df = pd.DataFrame(results)\n",
    "results_df.sort_values(by=\"win_rate\", ascending=False, inplace=True)\n",
    "results_df = results_df[results_df[\"games_played\"] > 1]\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|    | model                                                          |   games_won |   games_played |   win_rate |\n",
      "|---:|:---------------------------------------------------------------|------------:|---------------:|-----------:|\n",
      "| 12 | openai:gpt-4o:text                                             |          70 |             70 |   1        |\n",
      "|  4 | openai:gpt-4o-mini:vision                                      |          59 |             63 |   0.936508 |\n",
      "| 11 | openai:gpt-4o-mini:text                                        |          59 |             77 |   0.766234 |\n",
      "| 13 | mistral:pixtral-12b-2409:vision                                |          46 |             63 |   0.730159 |\n",
      "|  5 | mistral:pixtral-large-latest:vision                            |          45 |             70 |   0.642857 |\n",
      "| 10 | mistral:pixtral-12b-2409:text                                  |          52 |             91 |   0.571429 |\n",
      "|  0 | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:text   |           8 |             15 |   0.533333 |\n",
      "|  9 | openai:gpt-4o:vision                                           |          43 |            108 |   0.398148 |\n",
      "|  2 | anthropic:claude-3-sonnet-20240229:vision                      |          23 |             58 |   0.396552 |\n",
      "|  6 | anthropic:claude-3-haiku-20240307:vision                       |          34 |             86 |   0.395349 |\n",
      "|  3 | mistral:pixtral-large-latest:text                              |          25 |             70 |   0.357143 |\n",
      "|  7 | anthropic:claude-3-haiku-20240307:text                         |          18 |             61 |   0.295082 |\n",
      "|  1 | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision |           9 |             62 |   0.145161 |\n",
      "|  8 | anthropic:claude-3-sonnet-20240229:text                        |          16 |            120 |   0.133333 |\n"
     ]
    }
   ],
   "source": [
    "print(results_df.to_markdown())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|     | model_1                                                        | model_2                                                        |   games_won |   games_played |   win_rate |\n",
      "|----:|:---------------------------------------------------------------|:---------------------------------------------------------------|------------:|---------------:|-----------:|\n",
      "|   3 | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:text   | mistral:pixtral-large-latest:text                              |           1 |              2 |  0.5       |\n",
      "|   8 | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:text   | anthropic:claude-3-sonnet-20240229:text                        |           7 |              7 |  1         |\n",
      "|  16 | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision | anthropic:claude-3-sonnet-20240229:vision                      |           0 |              5 |  0         |\n",
      "|  17 | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision | mistral:pixtral-large-latest:text                              |           2 |              7 |  0.285714  |\n",
      "|  18 | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision | openai:gpt-4o-mini:vision                                      |           0 |              3 |  0         |\n",
      "|  19 | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision | mistral:pixtral-large-latest:vision                            |           0 |              4 |  0         |\n",
      "|  20 | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision | anthropic:claude-3-haiku-20240307:vision                       |           0 |              3 |  0         |\n",
      "|  21 | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision | anthropic:claude-3-haiku-20240307:text                         |           0 |              5 |  0         |\n",
      "|  22 | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision | anthropic:claude-3-sonnet-20240229:text                        |           6 |             13 |  0.461538  |\n",
      "|  23 | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision | openai:gpt-4o:vision                                           |           0 |              5 |  0         |\n",
      "|  24 | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision | mistral:pixtral-12b-2409:text                                  |           0 |              4 |  0         |\n",
      "|  25 | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision | openai:gpt-4o-mini:text                                        |           0 |              4 |  0         |\n",
      "|  26 | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision | openai:gpt-4o:text                                             |           0 |              3 |  0         |\n",
      "|  27 | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision | mistral:pixtral-12b-2409:vision                                |           0 |              5 |  0         |\n",
      "|  29 | anthropic:claude-3-sonnet-20240229:vision                      | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision |           5 |              5 |  1         |\n",
      "|  31 | anthropic:claude-3-sonnet-20240229:vision                      | mistral:pixtral-large-latest:text                              |           1 |              7 |  0.142857  |\n",
      "|  32 | anthropic:claude-3-sonnet-20240229:vision                      | openai:gpt-4o-mini:vision                                      |           0 |              3 |  0         |\n",
      "|  33 | anthropic:claude-3-sonnet-20240229:vision                      | mistral:pixtral-large-latest:vision                            |           0 |              5 |  0         |\n",
      "|  34 | anthropic:claude-3-sonnet-20240229:vision                      | anthropic:claude-3-haiku-20240307:vision                       |           5 |              6 |  0.833333  |\n",
      "|  35 | anthropic:claude-3-sonnet-20240229:vision                      | anthropic:claude-3-haiku-20240307:text                         |           4 |              6 |  0.666667  |\n",
      "|  36 | anthropic:claude-3-sonnet-20240229:vision                      | anthropic:claude-3-sonnet-20240229:text                        |           8 |              9 |  0.888889  |\n",
      "|  37 | anthropic:claude-3-sonnet-20240229:vision                      | openai:gpt-4o:vision                                           |           0 |              2 |  0         |\n",
      "|  38 | anthropic:claude-3-sonnet-20240229:vision                      | mistral:pixtral-12b-2409:text                                  |           0 |              3 |  0         |\n",
      "|  39 | anthropic:claude-3-sonnet-20240229:vision                      | openai:gpt-4o-mini:text                                        |           0 |              6 |  0         |\n",
      "|  40 | anthropic:claude-3-sonnet-20240229:vision                      | openai:gpt-4o:text                                             |           0 |              2 |  0         |\n",
      "|  41 | anthropic:claude-3-sonnet-20240229:vision                      | mistral:pixtral-12b-2409:vision                                |           0 |              4 |  0         |\n",
      "|  42 | mistral:pixtral-large-latest:text                              | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:text   |           1 |              2 |  0.5       |\n",
      "|  43 | mistral:pixtral-large-latest:text                              | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision |           5 |              7 |  0.714286  |\n",
      "|  44 | mistral:pixtral-large-latest:text                              | anthropic:claude-3-sonnet-20240229:vision                      |           6 |              7 |  0.857143  |\n",
      "|  46 | mistral:pixtral-large-latest:text                              | openai:gpt-4o-mini:vision                                      |           0 |              7 |  0         |\n",
      "|  47 | mistral:pixtral-large-latest:text                              | mistral:pixtral-large-latest:vision                            |           0 |              7 |  0         |\n",
      "|  48 | mistral:pixtral-large-latest:text                              | anthropic:claude-3-haiku-20240307:vision                       |           0 |              2 |  0         |\n",
      "|  49 | mistral:pixtral-large-latest:text                              | anthropic:claude-3-haiku-20240307:text                         |           0 |              4 |  0         |\n",
      "|  50 | mistral:pixtral-large-latest:text                              | anthropic:claude-3-sonnet-20240229:text                        |          13 |             14 |  0.928571  |\n",
      "|  51 | mistral:pixtral-large-latest:text                              | openai:gpt-4o:vision                                           |           0 |              3 |  0         |\n",
      "|  52 | mistral:pixtral-large-latest:text                              | mistral:pixtral-12b-2409:text                                  |           0 |              3 |  0         |\n",
      "|  53 | mistral:pixtral-large-latest:text                              | openai:gpt-4o-mini:text                                        |           0 |              7 |  0         |\n",
      "|  54 | mistral:pixtral-large-latest:text                              | openai:gpt-4o:text                                             |           0 |              4 |  0         |\n",
      "|  55 | mistral:pixtral-large-latest:text                              | mistral:pixtral-12b-2409:vision                                |           0 |              3 |  0         |\n",
      "|  57 | openai:gpt-4o-mini:vision                                      | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision |           3 |              3 |  1         |\n",
      "|  58 | openai:gpt-4o-mini:vision                                      | anthropic:claude-3-sonnet-20240229:vision                      |           3 |              3 |  1         |\n",
      "|  59 | openai:gpt-4o-mini:vision                                      | mistral:pixtral-large-latest:text                              |           7 |              7 |  1         |\n",
      "|  61 | openai:gpt-4o-mini:vision                                      | mistral:pixtral-large-latest:vision                            |           5 |              5 |  1         |\n",
      "|  62 | openai:gpt-4o-mini:vision                                      | anthropic:claude-3-haiku-20240307:vision                       |           5 |              5 |  1         |\n",
      "|  63 | openai:gpt-4o-mini:vision                                      | anthropic:claude-3-haiku-20240307:text                         |           4 |              4 |  1         |\n",
      "|  64 | openai:gpt-4o-mini:vision                                      | anthropic:claude-3-sonnet-20240229:text                        |           5 |              5 |  1         |\n",
      "|  65 | openai:gpt-4o-mini:vision                                      | openai:gpt-4o:vision                                           |          12 |             12 |  1         |\n",
      "|  66 | openai:gpt-4o-mini:vision                                      | mistral:pixtral-12b-2409:text                                  |           5 |              5 |  1         |\n",
      "|  67 | openai:gpt-4o-mini:vision                                      | openai:gpt-4o-mini:text                                        |           4 |              4 |  1         |\n",
      "|  68 | openai:gpt-4o-mini:vision                                      | openai:gpt-4o:text                                             |           0 |              4 |  0         |\n",
      "|  69 | openai:gpt-4o-mini:vision                                      | mistral:pixtral-12b-2409:vision                                |           5 |              5 |  1         |\n",
      "|  71 | mistral:pixtral-large-latest:vision                            | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision |           4 |              4 |  1         |\n",
      "|  72 | mistral:pixtral-large-latest:vision                            | anthropic:claude-3-sonnet-20240229:vision                      |           5 |              5 |  1         |\n",
      "|  73 | mistral:pixtral-large-latest:vision                            | mistral:pixtral-large-latest:text                              |           7 |              7 |  1         |\n",
      "|  74 | mistral:pixtral-large-latest:vision                            | openai:gpt-4o-mini:vision                                      |           0 |              5 |  0         |\n",
      "|  76 | mistral:pixtral-large-latest:vision                            | anthropic:claude-3-haiku-20240307:vision                       |           9 |             10 |  0.9       |\n",
      "|  77 | mistral:pixtral-large-latest:vision                            | anthropic:claude-3-haiku-20240307:text                         |           2 |              3 |  0.666667  |\n",
      "|  78 | mistral:pixtral-large-latest:vision                            | anthropic:claude-3-sonnet-20240229:text                        |           9 |              9 |  1         |\n",
      "|  79 | mistral:pixtral-large-latest:vision                            | openai:gpt-4o:vision                                           |           0 |              6 |  0         |\n",
      "|  80 | mistral:pixtral-large-latest:vision                            | mistral:pixtral-12b-2409:text                                  |           3 |              4 |  0.75      |\n",
      "|  81 | mistral:pixtral-large-latest:vision                            | openai:gpt-4o-mini:text                                        |           0 |              4 |  0         |\n",
      "|  82 | mistral:pixtral-large-latest:vision                            | openai:gpt-4o:text                                             |           0 |              6 |  0         |\n",
      "|  83 | mistral:pixtral-large-latest:vision                            | mistral:pixtral-12b-2409:vision                                |           6 |              7 |  0.857143  |\n",
      "|  85 | anthropic:claude-3-haiku-20240307:vision                       | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision |           3 |              3 |  1         |\n",
      "|  86 | anthropic:claude-3-haiku-20240307:vision                       | anthropic:claude-3-sonnet-20240229:vision                      |           1 |              6 |  0.166667  |\n",
      "|  87 | anthropic:claude-3-haiku-20240307:vision                       | mistral:pixtral-large-latest:text                              |           2 |              2 |  1         |\n",
      "|  88 | anthropic:claude-3-haiku-20240307:vision                       | openai:gpt-4o-mini:vision                                      |           0 |              5 |  0         |\n",
      "|  89 | anthropic:claude-3-haiku-20240307:vision                       | mistral:pixtral-large-latest:vision                            |           1 |             10 |  0.1       |\n",
      "|  91 | anthropic:claude-3-haiku-20240307:vision                       | anthropic:claude-3-haiku-20240307:text                         |           8 |              8 |  1         |\n",
      "|  92 | anthropic:claude-3-haiku-20240307:vision                       | anthropic:claude-3-sonnet-20240229:text                        |           5 |              7 |  0.714286  |\n",
      "|  93 | anthropic:claude-3-haiku-20240307:vision                       | openai:gpt-4o:vision                                           |           4 |             10 |  0.4       |\n",
      "|  94 | anthropic:claude-3-haiku-20240307:vision                       | mistral:pixtral-12b-2409:text                                  |           6 |             14 |  0.428571  |\n",
      "|  95 | anthropic:claude-3-haiku-20240307:vision                       | openai:gpt-4o-mini:text                                        |           3 |              8 |  0.375     |\n",
      "|  96 | anthropic:claude-3-haiku-20240307:vision                       | openai:gpt-4o:text                                             |           0 |              6 |  0         |\n",
      "|  97 | anthropic:claude-3-haiku-20240307:vision                       | mistral:pixtral-12b-2409:vision                                |           0 |              6 |  0         |\n",
      "|  99 | anthropic:claude-3-haiku-20240307:text                         | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision |           5 |              5 |  1         |\n",
      "| 100 | anthropic:claude-3-haiku-20240307:text                         | anthropic:claude-3-sonnet-20240229:vision                      |           2 |              6 |  0.333333  |\n",
      "| 101 | anthropic:claude-3-haiku-20240307:text                         | mistral:pixtral-large-latest:text                              |           4 |              4 |  1         |\n",
      "| 102 | anthropic:claude-3-haiku-20240307:text                         | openai:gpt-4o-mini:vision                                      |           0 |              4 |  0         |\n",
      "| 103 | anthropic:claude-3-haiku-20240307:text                         | mistral:pixtral-large-latest:vision                            |           1 |              3 |  0.333333  |\n",
      "| 104 | anthropic:claude-3-haiku-20240307:text                         | anthropic:claude-3-haiku-20240307:vision                       |           0 |              8 |  0         |\n",
      "| 106 | anthropic:claude-3-haiku-20240307:text                         | anthropic:claude-3-sonnet-20240229:text                        |           6 |             11 |  0.545455  |\n",
      "| 107 | anthropic:claude-3-haiku-20240307:text                         | openai:gpt-4o:vision                                           |           0 |              4 |  0         |\n",
      "| 108 | anthropic:claude-3-haiku-20240307:text                         | mistral:pixtral-12b-2409:text                                  |           0 |              4 |  0         |\n",
      "| 109 | anthropic:claude-3-haiku-20240307:text                         | openai:gpt-4o-mini:text                                        |           0 |              3 |  0         |\n",
      "| 110 | anthropic:claude-3-haiku-20240307:text                         | openai:gpt-4o:text                                             |           0 |              6 |  0         |\n",
      "| 111 | anthropic:claude-3-haiku-20240307:text                         | mistral:pixtral-12b-2409:vision                                |           0 |              3 |  0         |\n",
      "| 112 | anthropic:claude-3-sonnet-20240229:text                        | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:text   |           0 |              7 |  0         |\n",
      "| 113 | anthropic:claude-3-sonnet-20240229:text                        | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision |           7 |             13 |  0.538462  |\n",
      "| 114 | anthropic:claude-3-sonnet-20240229:text                        | anthropic:claude-3-sonnet-20240229:vision                      |           1 |              9 |  0.111111  |\n",
      "| 115 | anthropic:claude-3-sonnet-20240229:text                        | mistral:pixtral-large-latest:text                              |           1 |             14 |  0.0714286 |\n",
      "| 116 | anthropic:claude-3-sonnet-20240229:text                        | openai:gpt-4o-mini:vision                                      |           0 |              5 |  0         |\n",
      "| 117 | anthropic:claude-3-sonnet-20240229:text                        | mistral:pixtral-large-latest:vision                            |           0 |              9 |  0         |\n",
      "| 118 | anthropic:claude-3-sonnet-20240229:text                        | anthropic:claude-3-haiku-20240307:vision                       |           2 |              7 |  0.285714  |\n",
      "| 119 | anthropic:claude-3-sonnet-20240229:text                        | anthropic:claude-3-haiku-20240307:text                         |           5 |             11 |  0.454545  |\n",
      "| 121 | anthropic:claude-3-sonnet-20240229:text                        | openai:gpt-4o:vision                                           |           0 |              5 |  0         |\n",
      "| 122 | anthropic:claude-3-sonnet-20240229:text                        | mistral:pixtral-12b-2409:text                                  |           0 |             10 |  0         |\n",
      "| 123 | anthropic:claude-3-sonnet-20240229:text                        | openai:gpt-4o-mini:text                                        |           0 |              7 |  0         |\n",
      "| 124 | anthropic:claude-3-sonnet-20240229:text                        | openai:gpt-4o:text                                             |           0 |             11 |  0         |\n",
      "| 125 | anthropic:claude-3-sonnet-20240229:text                        | mistral:pixtral-12b-2409:vision                                |           0 |             12 |  0         |\n",
      "| 127 | openai:gpt-4o:vision                                           | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision |           5 |              5 |  1         |\n",
      "| 128 | openai:gpt-4o:vision                                           | anthropic:claude-3-sonnet-20240229:vision                      |           2 |              2 |  1         |\n",
      "| 129 | openai:gpt-4o:vision                                           | mistral:pixtral-large-latest:text                              |           3 |              3 |  1         |\n",
      "| 130 | openai:gpt-4o:vision                                           | openai:gpt-4o-mini:vision                                      |           0 |             12 |  0         |\n",
      "| 131 | openai:gpt-4o:vision                                           | mistral:pixtral-large-latest:vision                            |           6 |              6 |  1         |\n",
      "| 132 | openai:gpt-4o:vision                                           | anthropic:claude-3-haiku-20240307:vision                       |           6 |             10 |  0.6       |\n",
      "| 133 | openai:gpt-4o:vision                                           | anthropic:claude-3-haiku-20240307:text                         |           4 |              4 |  1         |\n",
      "| 134 | openai:gpt-4o:vision                                           | anthropic:claude-3-sonnet-20240229:text                        |           5 |              5 |  1         |\n",
      "| 136 | openai:gpt-4o:vision                                           | mistral:pixtral-12b-2409:text                                  |           9 |             25 |  0.36      |\n",
      "| 137 | openai:gpt-4o:vision                                           | openai:gpt-4o-mini:text                                        |           2 |             14 |  0.142857  |\n",
      "| 138 | openai:gpt-4o:vision                                           | openai:gpt-4o:text                                             |           0 |             12 |  0         |\n",
      "| 139 | openai:gpt-4o:vision                                           | mistral:pixtral-12b-2409:vision                                |           0 |              9 |  0         |\n",
      "| 141 | mistral:pixtral-12b-2409:text                                  | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision |           4 |              4 |  1         |\n",
      "| 142 | mistral:pixtral-12b-2409:text                                  | anthropic:claude-3-sonnet-20240229:vision                      |           3 |              3 |  1         |\n",
      "| 143 | mistral:pixtral-12b-2409:text                                  | mistral:pixtral-large-latest:text                              |           3 |              3 |  1         |\n",
      "| 144 | mistral:pixtral-12b-2409:text                                  | openai:gpt-4o-mini:vision                                      |           0 |              5 |  0         |\n",
      "| 145 | mistral:pixtral-12b-2409:text                                  | mistral:pixtral-large-latest:vision                            |           1 |              4 |  0.25      |\n",
      "| 146 | mistral:pixtral-12b-2409:text                                  | anthropic:claude-3-haiku-20240307:vision                       |           8 |             14 |  0.571429  |\n",
      "| 147 | mistral:pixtral-12b-2409:text                                  | anthropic:claude-3-haiku-20240307:text                         |           4 |              4 |  1         |\n",
      "| 148 | mistral:pixtral-12b-2409:text                                  | anthropic:claude-3-sonnet-20240229:text                        |          10 |             10 |  1         |\n",
      "| 149 | mistral:pixtral-12b-2409:text                                  | openai:gpt-4o:vision                                           |          16 |             25 |  0.64      |\n",
      "| 151 | mistral:pixtral-12b-2409:text                                  | openai:gpt-4o-mini:text                                        |           2 |             11 |  0.181818  |\n",
      "| 152 | mistral:pixtral-12b-2409:text                                  | openai:gpt-4o:text                                             |           0 |              5 |  0         |\n",
      "| 153 | mistral:pixtral-12b-2409:text                                  | mistral:pixtral-12b-2409:vision                                |           0 |              2 |  0         |\n",
      "| 155 | openai:gpt-4o-mini:text                                        | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision |           4 |              4 |  1         |\n",
      "| 156 | openai:gpt-4o-mini:text                                        | anthropic:claude-3-sonnet-20240229:vision                      |           6 |              6 |  1         |\n",
      "| 157 | openai:gpt-4o-mini:text                                        | mistral:pixtral-large-latest:text                              |           7 |              7 |  1         |\n",
      "| 158 | openai:gpt-4o-mini:text                                        | openai:gpt-4o-mini:vision                                      |           0 |              4 |  0         |\n",
      "| 159 | openai:gpt-4o-mini:text                                        | mistral:pixtral-large-latest:vision                            |           4 |              4 |  1         |\n",
      "| 160 | openai:gpt-4o-mini:text                                        | anthropic:claude-3-haiku-20240307:vision                       |           5 |              8 |  0.625     |\n",
      "| 161 | openai:gpt-4o-mini:text                                        | anthropic:claude-3-haiku-20240307:text                         |           3 |              3 |  1         |\n",
      "| 162 | openai:gpt-4o-mini:text                                        | anthropic:claude-3-sonnet-20240229:text                        |           7 |              7 |  1         |\n",
      "| 163 | openai:gpt-4o-mini:text                                        | openai:gpt-4o:vision                                           |          12 |             14 |  0.857143  |\n",
      "| 164 | openai:gpt-4o-mini:text                                        | mistral:pixtral-12b-2409:text                                  |           9 |             11 |  0.818182  |\n",
      "| 166 | openai:gpt-4o-mini:text                                        | openai:gpt-4o:text                                             |           0 |              7 |  0         |\n",
      "| 167 | openai:gpt-4o-mini:text                                        | mistral:pixtral-12b-2409:vision                                |           2 |              2 |  1         |\n",
      "| 169 | openai:gpt-4o:text                                             | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision |           3 |              3 |  1         |\n",
      "| 170 | openai:gpt-4o:text                                             | anthropic:claude-3-sonnet-20240229:vision                      |           2 |              2 |  1         |\n",
      "| 171 | openai:gpt-4o:text                                             | mistral:pixtral-large-latest:text                              |           4 |              4 |  1         |\n",
      "| 172 | openai:gpt-4o:text                                             | openai:gpt-4o-mini:vision                                      |           4 |              4 |  1         |\n",
      "| 173 | openai:gpt-4o:text                                             | mistral:pixtral-large-latest:vision                            |           6 |              6 |  1         |\n",
      "| 174 | openai:gpt-4o:text                                             | anthropic:claude-3-haiku-20240307:vision                       |           6 |              6 |  1         |\n",
      "| 175 | openai:gpt-4o:text                                             | anthropic:claude-3-haiku-20240307:text                         |           6 |              6 |  1         |\n",
      "| 176 | openai:gpt-4o:text                                             | anthropic:claude-3-sonnet-20240229:text                        |          11 |             11 |  1         |\n",
      "| 177 | openai:gpt-4o:text                                             | openai:gpt-4o:vision                                           |          12 |             12 |  1         |\n",
      "| 178 | openai:gpt-4o:text                                             | mistral:pixtral-12b-2409:text                                  |           5 |              5 |  1         |\n",
      "| 179 | openai:gpt-4o:text                                             | openai:gpt-4o-mini:text                                        |           7 |              7 |  1         |\n",
      "| 181 | openai:gpt-4o:text                                             | mistral:pixtral-12b-2409:vision                                |           4 |              4 |  1         |\n",
      "| 183 | mistral:pixtral-12b-2409:vision                                | together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision |           5 |              5 |  1         |\n",
      "| 184 | mistral:pixtral-12b-2409:vision                                | anthropic:claude-3-sonnet-20240229:vision                      |           4 |              4 |  1         |\n",
      "| 185 | mistral:pixtral-12b-2409:vision                                | mistral:pixtral-large-latest:text                              |           3 |              3 |  1         |\n",
      "| 186 | mistral:pixtral-12b-2409:vision                                | openai:gpt-4o-mini:vision                                      |           0 |              5 |  0         |\n",
      "| 187 | mistral:pixtral-12b-2409:vision                                | mistral:pixtral-large-latest:vision                            |           1 |              7 |  0.142857  |\n",
      "| 188 | mistral:pixtral-12b-2409:vision                                | anthropic:claude-3-haiku-20240307:vision                       |           6 |              6 |  1         |\n",
      "| 189 | mistral:pixtral-12b-2409:vision                                | anthropic:claude-3-haiku-20240307:text                         |           3 |              3 |  1         |\n",
      "| 190 | mistral:pixtral-12b-2409:vision                                | anthropic:claude-3-sonnet-20240229:text                        |          12 |             12 |  1         |\n",
      "| 191 | mistral:pixtral-12b-2409:vision                                | openai:gpt-4o:vision                                           |           9 |              9 |  1         |\n",
      "| 192 | mistral:pixtral-12b-2409:vision                                | mistral:pixtral-12b-2409:text                                  |           2 |              2 |  1         |\n",
      "| 193 | mistral:pixtral-12b-2409:vision                                | openai:gpt-4o-mini:text                                        |           0 |              2 |  0         |\n",
      "| 194 | mistral:pixtral-12b-2409:vision                                | openai:gpt-4o:text                                             |           0 |              4 |  0         |\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "\n",
    "for model_1 in unique_models:\n",
    "    for model_2 in unique_models:\n",
    "        games_played = 0\n",
    "        wins = 0\n",
    "        for i, row in df.iterrows():\n",
    "            if row[\"player_1_model\"] == model_1 and row[\"player_2_model\"] == model_2 and model_1 != model_2:\n",
    "                games_played += 1\n",
    "                if row[\"player_1_won\"]:\n",
    "                    wins += 1\n",
    "                continue\n",
    "            if row[\"player_2_model\"] == model_1 and row[\"player_1_model\"] == model_2 and model_1 != model_2:\n",
    "                games_played += 1\n",
    "                if row[\"player_2_won\"]:\n",
    "                    wins += 1\n",
    "                continue\n",
    "        try:\n",
    "            win_rate = wins / games_played\n",
    "        except:\n",
    "            win_rate = 0\n",
    "        results.append({\n",
    "            \"model_1\": model_1,\n",
    "            \"model_2\": model_2,\n",
    "            \"games_won\": wins,\n",
    "            \"games_played\": games_played,\n",
    "            \"win_rate\": win_rate\n",
    "        })\n",
    "results_df = pd.DataFrame(results)\n",
    "results_df = results_df[results_df[\"games_played\"] > 1]\n",
    "print(results_df.to_markdown())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display results df in a matrix\n",
    "\n",
    "results_matrix = results_df.pivot(index=\"model_1\", columns=\"model_2\", values=\"win_rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| model_1                                                        |   anthropic:claude-3-haiku-20240307:text |   anthropic:claude-3-haiku-20240307:vision |   anthropic:claude-3-sonnet-20240229:text |   anthropic:claude-3-sonnet-20240229:vision |   mistral:pixtral-12b-2409:text |   mistral:pixtral-12b-2409:vision |   mistral:pixtral-large-latest:text |   mistral:pixtral-large-latest:vision |   openai:gpt-4o-mini:text |   openai:gpt-4o-mini:vision |   openai:gpt-4o:text |   openai:gpt-4o:vision |   together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:text |   together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision |\n",
      "|:---------------------------------------------------------------|-----------------------------------------:|-------------------------------------------:|------------------------------------------:|--------------------------------------------:|--------------------------------:|----------------------------------:|------------------------------------:|--------------------------------------:|--------------------------:|----------------------------:|---------------------:|-----------------------:|---------------------------------------------------------------:|-----------------------------------------------------------------:|\n",
      "| anthropic:claude-3-haiku-20240307:text                         |                               nan        |                                   0        |                                  0.545455 |                                    0.333333 |                        0        |                          0        |                           1         |                              0.333333 |                  0        |                           0 |                    0 |               0        |                                                          nan   |                                                         1        |\n",
      "| anthropic:claude-3-haiku-20240307:vision                       |                                 1        |                                 nan        |                                  0.714286 |                                    0.166667 |                        0.428571 |                          0        |                           1         |                              0.1      |                  0.375    |                           0 |                    0 |               0.4      |                                                          nan   |                                                         1        |\n",
      "| anthropic:claude-3-sonnet-20240229:text                        |                                 0.454545 |                                   0.285714 |                                nan        |                                    0.111111 |                        0        |                          0        |                           0.0714286 |                              0        |                  0        |                           0 |                    0 |               0        |                                                            0   |                                                         0.538462 |\n",
      "| anthropic:claude-3-sonnet-20240229:vision                      |                                 0.666667 |                                   0.833333 |                                  0.888889 |                                  nan        |                        0        |                          0        |                           0.142857  |                              0        |                  0        |                           0 |                    0 |               0        |                                                          nan   |                                                         1        |\n",
      "| mistral:pixtral-12b-2409:text                                  |                                 1        |                                   0.571429 |                                  1        |                                    1        |                      nan        |                          0        |                           1         |                              0.25     |                  0.181818 |                           0 |                    0 |               0.64     |                                                          nan   |                                                         1        |\n",
      "| mistral:pixtral-12b-2409:vision                                |                                 1        |                                   1        |                                  1        |                                    1        |                        1        |                        nan        |                           1         |                              0.142857 |                  0        |                           0 |                    0 |               1        |                                                          nan   |                                                         1        |\n",
      "| mistral:pixtral-large-latest:text                              |                                 0        |                                   0        |                                  0.928571 |                                    0.857143 |                        0        |                          0        |                         nan         |                              0        |                  0        |                           0 |                    0 |               0        |                                                            0.5 |                                                         0.714286 |\n",
      "| mistral:pixtral-large-latest:vision                            |                                 0.666667 |                                   0.9      |                                  1        |                                    1        |                        0.75     |                          0.857143 |                           1         |                            nan        |                  0        |                           0 |                    0 |               0        |                                                          nan   |                                                         1        |\n",
      "| openai:gpt-4o-mini:text                                        |                                 1        |                                   0.625    |                                  1        |                                    1        |                        0.818182 |                          1        |                           1         |                              1        |                nan        |                           0 |                    0 |               0.857143 |                                                          nan   |                                                         1        |\n",
      "| openai:gpt-4o-mini:vision                                      |                                 1        |                                   1        |                                  1        |                                    1        |                        1        |                          1        |                           1         |                              1        |                  1        |                         nan |                    0 |               1        |                                                          nan   |                                                         1        |\n",
      "| openai:gpt-4o:text                                             |                                 1        |                                   1        |                                  1        |                                    1        |                        1        |                          1        |                           1         |                              1        |                  1        |                           1 |                  nan |               1        |                                                          nan   |                                                         1        |\n",
      "| openai:gpt-4o:vision                                           |                                 1        |                                   0.6      |                                  1        |                                    1        |                        0.36     |                          0        |                           1         |                              1        |                  0.142857 |                           0 |                    0 |             nan        |                                                          nan   |                                                         1        |\n",
      "| together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:text   |                               nan        |                                 nan        |                                  1        |                                  nan        |                      nan        |                        nan        |                           0.5       |                            nan        |                nan        |                         nan |                  nan |             nan        |                                                          nan   |                                                       nan        |\n",
      "| together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision |                                 0        |                                   0        |                                  0.461538 |                                    0        |                        0        |                          0        |                           0.285714  |                              0        |                  0        |                           0 |                    0 |               0        |                                                          nan   |                                                       nan        |\n"
     ]
    }
   ],
   "source": [
    "print(results_matrix.to_markdown())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('anthropic:claude-3-haiku-20240307:text', 'together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:text'), ('anthropic:claude-3-haiku-20240307:vision', 'together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:text'), ('anthropic:claude-3-sonnet-20240229:vision', 'together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:text'), ('mistral:pixtral-12b-2409:text', 'together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:text'), ('mistral:pixtral-12b-2409:vision', 'together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:text'), ('mistral:pixtral-large-latest:vision', 'together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:text'), ('openai:gpt-4o-mini:text', 'together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:text'), ('openai:gpt-4o-mini:vision', 'together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:text'), ('openai:gpt-4o:text', 'together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:text'), ('openai:gpt-4o:vision', 'together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:text'), ('together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:text', 'together:meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo:vision')]\n"
     ]
    }
   ],
   "source": [
    "#We want to select the matches that did not happen. \n",
    "# Filter results_matrix to only keep the NaN values\n",
    "results_matrix.isna()\n",
    "# We want to get the index of lines and columns that are True\n",
    "missing_matches = results_matrix.isna().stack()[lambda x: x].index.tolist()\n",
    "\n",
    "# We want to delete the matches with same values\n",
    "missing_matches = [match for match in missing_matches if match[0] != match[1]]\n",
    "\n",
    "# We want to remove symetrc redudant matches\n",
    "missing_matches_unique = []\n",
    "for match in missing_matches:\n",
    "    if (match[1], match[0]) not in missing_matches_unique:\n",
    "        missing_matches_unique.append(match)\n",
    "\n",
    "print(missing_matches_unique)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in /Users/adle/Downloads/llm-colosseum/.venv/lib/python3.13/site-packages (0.13.2)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /Users/adle/Downloads/llm-colosseum/.venv/lib/python3.13/site-packages (from seaborn) (1.26.4)\n",
      "Requirement already satisfied: pandas>=1.2 in /Users/adle/Downloads/llm-colosseum/.venv/lib/python3.13/site-packages (from seaborn) (2.2.1)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /Users/adle/Downloads/llm-colosseum/.venv/lib/python3.13/site-packages (from seaborn) (3.9.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/adle/Downloads/llm-colosseum/.venv/lib/python3.13/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/adle/Downloads/llm-colosseum/.venv/lib/python3.13/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/adle/Downloads/llm-colosseum/.venv/lib/python3.13/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.54.1)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /Users/adle/Downloads/llm-colosseum/.venv/lib/python3.13/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.7)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/adle/Downloads/llm-colosseum/.venv/lib/python3.13/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.2)\n",
      "Requirement already satisfied: pillow>=8 in /Users/adle/Downloads/llm-colosseum/.venv/lib/python3.13/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/adle/Downloads/llm-colosseum/.venv/lib/python3.13/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/adle/Downloads/llm-colosseum/.venv/lib/python3.13/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/adle/Downloads/llm-colosseum/.venv/lib/python3.13/site-packages (from pandas>=1.2->seaborn) (2024.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /Users/adle/Downloads/llm-colosseum/.venv/lib/python3.13/site-packages (from pandas>=1.2->seaborn) (2024.2)\n",
      "Requirement already satisfied: six>=1.5 in /Users/adle/Downloads/llm-colosseum/.venv/lib/python3.13/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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48CA7duyQWf/6dW1sbAyAoaGhzOfV39+fpUuXSj+H1atX5+rVq/z444/07duXChUqsG3bNpo3b07FihUJCAggNDQUHR2dN+b7uszMTGlvciF1dfUPPlz+30pITCA3NxdDQ0OZ9YaGhty5c7vE+VRUV0ZZSULSaz25yRk5mOkoPvbHyZlsOH2f+4npaKkq4+VgzMx2NkzZH0NCesFjI1GPX3L2fhLPUrMwqaDGVy6VmdCyBmGXbpGXl0cFPdkbOBX09Hn2KFZROAAy0lJYOPRLcnKyUVJSwnvgN1g7u8qkuXb+FEEBc8jOyqSCniH9ZyxFW0evxHVRWnX6Lj5GzPelnJ/1yZ5DKPs6/a/H+1gxhWJ8RkOZS4PoCRbKrRs3buDj40ONGjXQ0dGRDj0t+i4yZ+dXkxdUrlwZQOYZx+IU3a+wofz6fq6ur/5AJycn8+jRIxo3biyTpnHjxkRHyz4PV3QoqoqKCq6urnJpChX2BBenS5cuMo2UyMhI6tSpI9cLWJwnT54wePBgbGxs0NXVRUdHh5SUFJk6fJvo6GgaNmwos+714bYXL14kMDCQChUqSBcPDw/y8vK4c+cO8+bNk9lWNL6vry8RERGEhYVha2tL9+7dycjIAApuCBTuU3QSKy0tLWkDGArOfdHz92+P28fHh9TUVEJCQt6pAXz8+HGZ49u+fbtcmpEjR3L58mV27txZ4nxfd/nyZTp16oS/vz/t2rUDCoZu9+rVi9mzZ2Nra/uv8y70pvPxrrZs2YKenh6dO3eWWf/6da1Iamoqt27dYuDAgTJ1+91330kfi4CCa3HChAnMnTuX8ePH06RJk3cu5/z589HV1ZVZ5s+f/875lAc3n6dx8k4CsQkZXHuayopjd3mZmUMrm1c/kE/fSyTiYTIPEjM4/yCZpUfvYGWkhZG26r+KqaahxajFPzF8/g+07TmIgz+v4fYV2R6oGk51GLX4J4bMXY2tSwN2Lp9V7DOqQtkT51AQSkE5Gw4teoKFcsvb2xsLCws2bNiAmZkZeXl51KxZU/rsIYCq6qsfVYXDK4sbGlxU0f0K9319P21t7fcpfoloahY/C2tppO/bty/x8fGsWLECCwsL1NXVcXNzk9ahklLBl2HR97/9m0nAUlJSGDp0KH5+fnLbqlWrxrBhw+jevbt0nZmZmfTfhY0OGxsbGjVqhL6+Pnv27MHHx4cDBw5Iy1P02BWdv6LH8LbjLo6Xlxfbtm0jPDxcpjdZSUlJ7h15RevJ1dVV5rnq13tLR40axf79+zl27BhVq1aVrjc1NSUrK4vExESZ3uAnT57I9VxevXqV1q1bM2TIEGbMmCFd//LlS86dO0dERIR0+HleXh75+fmoqKgQEhJCkyZNUFZWlpl1urg4bzofpqamcjNKF+b5ej75+fls2rSJ3r17v7VnX5GUlBQANmzYIHcTRllZWfrvvLw8Tp48ibKyMjdv3nznOABTp05l3LhxMus+1V5gAH09fZSVleUes4iPj8fIyKjE+bzMzCU3Lx9dDdmfGjoaKiSmyz/nq0huPtx7kY5JxeLP8bOULJIzcjAxMkJJSYmUxBcy21MSE+R6FotSUlLC0LTgc2NmacPTh/cI27uDGk51pGnUNDQxNK2KoWlVqtk6sczPl/N/H6Cnu2OJjqO06vRdfIyY7ytXovbJnkMo+zr9r8f7WDEFAURPsFBOxcfHExMTw4wZM2jdujUODg5vnCxKETU1tVKbZVhHRwczMzNOnjwps/7kyZM4Osr+gS6c0AggJyeH8+fP4+DgoDBfZ2dnjhw5UuJyODs7ExkZyYsXL96e+P/L5+fnh5eXF05OTqirq/P8+XPp9sLhokUnlXr9dTIODg5yDZ+ixwhQt25drl69irW1tdyipqaGgYGBzDpFQ3ahoOGUn58vHZpqYWEh3adKlSolOuaSHHdxhg8fzoIFC/jiiy9kJjQzNjYmLi5OpiFctJ40NTVljq9ixYrS4xk1ahR79uzh77//lk7UVKhevXqoqqrKXAMxMTHExsbK9LZfuXKFli1b0rdvX77//nuZPHR0dLh06RKRkZHSZdiwYdjZ2REZGUnDhg1RU1OjXr16MnHy8vI4cuSIwkm0Cr1+Ptzc3Lh06ZJMr/vhw4fR0dGR+xyEhYVx8+ZNBg4cWGz+hQobyUU/ryYmJpiZmXH79m25a6poPS5evJhr164RFhZGcHAwmzdvfmO+iqirq6OjoyOzfMqNYFU1NRwcnThz+tXz3Hl5eZw5E45z7Tpv2FNWbl4+d1+k4WhaUbpOAjiZVuDm87QS5SGRQFU9jTc2mvU1VamgrkyORAmzGnbcuvxqkrq8vDxuXT5PNduSN3Ty8/LJzX7zDa38/Hxy3pKmqNKq03fxMWK+t0/4HELZ1+l/Pd7HiikUo5zNDi16goVySV9fH0NDQ9avX0/lypWJjY2Vm4zpbSwtLUlJSeHIkSPUrl0bLS0ttLSKn+zlbSZOnIi/vz9WVla4uLiwefNmIiMj5Ya+rlmzBhsbGxwcHFi+fDkJCQkMGDBAYZ5Tp06lVq1ajBgxgmHDhqGmpkZoaChfffUVRkZG7Nmzh6lTp0qHjvr4+DBv3jw6d+7M/PnzqVy5MhEREZiZmSlszNjY2LB161ZcXV1JTk5m4sSJMj2qmpqaNGrUiAULFlC9enWePn0q08sIBTMOL126lIkTJzJo0CDOnz9PYGCgTJrJkyfTqFEjRo0axaBBg9DW1ubq1ascPnxY4at8AG7fvk1QUBDt2rXD2NiYBw8esGDBAjQ1NfHy8nrr+XiTtx33m4wePZrc3Fw6duzIwYMHadKkCS1atODZs2csWrSIL7/8kuDgYA4ePCh99rQ4I0eOZMeOHfz+++9UrFhR+tysrq4umpqa6OrqMnDgQMaNG4eBgQE6OjqMHj0aNzc3GjVqBBQMgW7VqhUeHh6MGzdOmoeysjLGxsYoKSlJnzEuVKlSJens5IXGjRtH3759cXV1pUGDBgQEBJCamiqdtbkk56Ndu3Y4OjrSu3dvFi1aRFxcHDNmzGDkyJFyjcaNGzfSsGFDubIBctd1pUqV0NTUJDg4mKpVq6KhoYGuri6zZ8/Gz88PXV1dPD09yczMlM42PW7cOCIiIpg5cya//fYbjRs3ZtmyZYwZM0Y6k3Zx+f4X9O7bn2+nTcbJqSY1azmzbesW0tPT6dyl69t3LuLgtecMcTPnTnwat+PT8LA3Rl1ZiWO3C260DXUzJyE9m18iC667zjVNuPk8lScpWWipKtPB0RgjbTWO3izoJVJXUaJLLRPOxiaRlJFNpQrq9KxTmScvs3jyMpPGHb9i15r5VKlhR1VrB04d+I2szAzqtSh43OHX1fPQMTDCo9cQAML2bKeKlR0GJmbkZGdzPeI0kcdD+GLQNwBkZaRzdPc27F3dqahvSNrLJE4H7yX5xTNqurX4KHX6KcfU1lTDytxY+n/LKoY421YhITmN+3Elu9H8KZ9DKPs6/a/H+1gxy1JpfC7KxGc0lLk0iEawUC4pKSmxc+dO/Pz8qFmzJnZ2dqxcufKdXnHi7u7OsGHD6NGjB/Hx8fj7+8u8Juld+fn5kZSUxPjx43n69CmOjo7s27cPGxsbmXQLFixgwYIFREZGYm1tzb59+4odMmRra0tISAjTpk2jQYMGaGpq0rBhQ3x8fICCWYCLzlqtpqZGSEgI48ePx8vLi5ycHBwdHWUm5ipq48aNDBkyhLp162Jubs68efNkZlGGggnHBg4cSL169bCzs2PRokXS502hYDjzrl27+Oabb1i1ahUNGjRg3rx5Mg17Z2dnwsLCmD59Ok2bNiU/Px8rKyt69OhRbH1qaGhw/PhxAgICSEhIwMTEhGbNmnHq1CkqVapU7H4lUZLjfpOxY8eSl5eHl5cXwcHBuLu7s3btWubNm8fcuXPp1q0bEyZMkHm1liLr1q0DkLtuN2/eTL9+/QBYvnw5SkpKdOvWjczMTDw8PGQmSvvtt9949uwZ27ZtY9u2bdL1FhYWJXqVVKEePXrw7NkzZs6cSVxcnPQVX4VDt0tyPpSVldm/fz/Dhw/Hzc0NbW1t+vbtKzMpGRRct7t27WLFihUKy/L6da2iosLKlSuZM2cOM2fOpGnTphw9epRBgwahpaXF4sWLmThxItra2tSqVYuxY8eSkZHB119/Tb9+/fD29gYKXvH0559/0rt3b44dO1Zsvv8Fnu29SHjxgrWrV/L8+TPs7B1Y++NPGL7j8MQz9xKpqK5Mt9qm6GqoEJuQzuLQO9LXHhlqq1H0SQBtNWUGNjJHV0OF1Kxc7r5IZ07ITR4lF4wWyMvPx1xPk6Y19NFSVSYhPYfLj1/yW1QcLa0NcHZvRWpyIkd+2czLxBdUtrSm37RF0qG0Sc+fyMwcnpWZzr6flpMU/wxVNXWMq1Tjq9HTcXYveFxBoqTEs0exXFh6iLSXSWhV1KGKlT2DZ6/CxFx25EVZ1emnHLOuowUhP42R/n/RhIJJHLfuO80Q/23F7SbjUz6HUPZ1+l+P97FilqXS+FyUic+oF7c0SPJffxBNEIRP0t27d6levToRERFy75QVBEF4GwWv2/2gBgddLNN4nWq+382tf6OjU+Uyj1mW9OuX/DV0pWVr4PQyjfdfP4flgcZH6NLTrFO2n430CMWj3kqTZtOZpZJP+vE5b0/0CRA9wYIgCIIgCIIgCOWZGA4tCIIgCIIgCIIglBuiESwIwqfI0tJS7jU6giAIgiAIgiC8G9EIFgRBEARBEARBKM+UytfEWKIRLAiCIAiCIAiCUJ6Vs+HQ5etoBUEQBEEQBEEQhHJN9AQLgiAIgiAIgiCUZ+XsPcGiESwIgiAIgiAIglCelbPh0KIRLAiCIAhlTLPOqDKPmR6xukzjRUQ/LdN401pZl2k8gJXHb5VpPL+mVmUa72NoVM2wTOPtiIgt03i96lQr03gfw8Yzd8s03sjGlmUaT/hvEI1gQRAEQRAEQRCE8kwMhxYEQRAEQRAEQRDKDTEcWhAEQRAEQRAEQSg3yllPcPlq8guCIAiCIAiCIAjlmugJFgRBEARBEARBKM/K2XDo8nW0gvCOLC0tCQgI+Cixjx49ikQiITEx8YPkP2vWLFxcXD5I3kV96ON4k8DAQPT09N47H4lEwt69ewG4e/cuEomEyMjI985X+DDe5XP7MT/jgiAIgvDJkEhKZ/lMiEawIFB6jaXS5O7uzuPHj9HV1f3YRfkkDB06FCsrKzQ1NTE2NqZTp05cu3atTGI/fvyY9u3bf9AY8+fPp379+lSsWJFKlSrRuXNnYmJiZNJkZGQwcuRIDA0NqVChAt26dePJkyfS7RcvXsTHxwdzc3M0NTVxcHBgxYoVxcY8efIkKioqCm+GrFmzBktLSzQ0NGjYsCH//POPzPaSnI/Y2Fg6dOiAlpYWlSpVYuLEieTk5Ei39+vXD4lEIrc4OTm9S9XJOXv2LEOGDCn1tB9T47pW/BYwlNsh35MesRrvFs4fu0gl1rNhVQ6Nb8J5/1bsGNqAmlV0ik27eWA9Ln/XVm5Z29tFmqaNYyXW96vLiWnNufxdW+xMK8jkcWBPEIN7dOCrto2YOLwP16MvFxsv9s4tFsycwOAeHejcoi77ft0ul6Zw2+vLjwHzAbh69A+CpvUjcFQn9i0Yy7M7MXJ5KHLrbBgbh3lxeN0cmfUX/tjGb/5D2OLXha3junMwYBpP7/z777qdO7bTvm0r6tephW/Pr7gUFfWv83qbD3md7v3tf/Tq7IFns3qMHNCLa1cuFZv27u2bzJryDb06e9C6US127dz61vwvHP6ddWO/Zkl/L372H82jW8XXeczZ42z5dgQBQzqzbKA3m6cN5fKJwzJpsjLSObxlFWtG+7C0fwd+mjSQiCN/lPyAiyjLc/gh4108so/NE/uwZkhHgub6EXe7ZNf19TNHWTnAg/2rZpVKOcra5/z9/V8mGsGCUMqys7NLJR81NTVMTU2RfEZ31T6kevXqsXnzZqKjozl06BD5+fm0a9eO3NzcDx7b1NQUdXX1DxojLCyMkSNHcvr0aQ4fPkx2djbt2rUjNTVVmuabb77hjz/+4NdffyUsLIxHjx7RtWtX6fbz589TqVIltm3bxpUrV5g+fTpTp05l9Wr598MmJibSp08fWrduLbctKCiIcePG4e/vz4ULF6hduzYeHh48ffrqva9vOx+5ubl06NCBrKwsTp06xZYtWwgMDGTmzJnSPFasWMHjx4+ly/379zEwMOCrr756r7o0NjZGS0ur1NN+TNqa6ly6/pCx84M+dlHeiWdNEya1t2Nd6G2+WnuGmLiX/NivLgbaqgrTj9lxkeYLwqRLp5WnyMnN49DlVzd7NNWUuXAvkeWHbsrtf+LvQ2xau4ye/YawbMMOLK1smD1xJIkJLxTGy8zMwLRyFfoM8UPfwEhhmiU/bmPzrhDpMnvJOgDcm7fl9rkwzvy2gTode9Fp2ioMqtYgeNW3pCcnvrFeXj5/wj+7fsLEWv6Gj65JFdx6DqfLt2vpOGExFQwrEbxiBukvk96YpyLBBw+wZNF8ho4Yyc5f92BnZ8/woQOJj49/57xK4kNdp6GHg/lhxWL6DBrGD1t+wcrGlsljh5LwQvFxZGRkULlKVQaNHIuBoeLzWlT06aP8vf1HGnf5mn7fraNStRr8snAqqUkJCtNrauvg9kUvvvZfQf95P1KrmQcH1i/hdtRZaZq/t//A7Yvn8B4+hUGLNuLq2ZXDW1Zz9O8j73TsZX0OP1S86/8c5XjQehp+4UtP/zUYmdfg92XTSXvLZyX5eRzHf9mAmW3N94r/MX02398SpdJZPhOfT0kF4Q2Cg4Np0qQJenp6GBoa0rFjR27dugW8Gr66e/duWrZsiZaWFrVr1yY8PBwoGK7bv39/kpKSpD1Rs2bNkuadlpbGgAEDqFixItWqVWP9+vXSbYV5BwUF0bx5czQ0NNi+fTt5eXnMmTOHqlWroq6ujouLC8HBwXL77dy5E3d3dzQ0NKhZsyZhYWHSNIqGEZ88eZIWLVqgpaWFvr4+Hh4eJCQo/iMN8ODBA3x8fDAwMEBbWxtXV1fOnDmjMO3Zs2dp27YtRkZG6Orq0rx5cy5cuCBX5qLDgBMTE5FIJBw9elS67sCBA9ja2qKpqUnLli25e/euXKwTJ07QtGlTNDU1MTc3x8/PT6axp8iQIUNo1qwZlpaW1K1bl++++4779+8rzP91hw4dwsHBgQoVKuDp6cnjx49LfNwgOxz6dbm5uQwYMAB7e3tiY2MVDjMPCAjA0tLyjWUMDg6mX79+ODk5Ubt2bQIDA4mNjeX8+fMAJCUlsXHjRpYtW0arVq2kjdBTp05x+vRpAAYMGMCKFSto3rw5NWrU4Ouvv6Z///7s3r1bLt6wYcPo1asXbm5uctuWLVvG4MGD6d+/P46Ojvzwww9oaWmxadMmaZq3nY+QkBCuXr3Ktm3bcHFxoX379sydO5c1a9aQlZUFgK6uLqamptLl3LlzJCQk0L9//2Lryd3dncmTJ8use/bsGaqqqhw7dgyQHeKcn5/PrFmzqFatGurq6piZmeHn5yfd9/Xh0LGxsXTq1IkKFSqgo6ND9+7dZXrbC8/v1q1bsbS0RFdXl549e/Ly5ctiy1waQk5eZfba/ewL/bA9QKWtT2MLfjv3gL0XHnH7WSpz9kWTkZ1Ll3pVFKZPTs8hPiVLurhZGZKRnUdIkUbwH5GP+SH0NuG35H+U//7rdtp16ELr9p0wt6zB8HHTUdfQ4MiB3xXGs7F3ot/wb2ja2gMVVcUNc109ffQNjaTL2fBjmJpVpaZLPS7/tQe7xp7YurdD36wajXuNQkVVneunQoqtk7y8XI5uWkRd76/RMaost92qQUuqONRBx7gy+mYWNPxyCNkZaSQ8vFNsnsXZumUzXb/sTucu3bCytmaG/2w0NDTYu3vXO+dVEh/qOv3tfz/j1akbnh27YFndirGTZ6KuoUnw/j0K09s71mTo6PG0atseVVW1t+Z/9uAuardsj3NzT4yqWODRfwyq6upcCjukMH01x9rY1m+CURUL9E3McPXsSiXzGjyIuSJN8/DGVWo2bUs1x9roGpvi0qoDlapZcfnSu9VNWZ/DDxUv4tBuajbzxLGpB4ZVLGjVxw8VNXWuHldcx1DwWTm0fiGNOvVG11j+s/K5+Gy+v8VwaEH4/KSmpjJu3DjOnTvHkSNHUFJSokuXLuTl5UnTTJ8+nQkTJhAZGYmtrS0+Pj7k5OTg7u5OQEAAOjo60h6pCRMmSPdbunQprq6uREREMGLECIYPHy43THXKlCmMGTOG6OhoPDw8WLFiBUuXLmXJkiVERUXh4eHBF198wY0bN2T2mzhxIuPHjyciIgI3Nze8vb2LvdsaGRlJ69atcXR0JDw8nBMnTuDt7S3teQsMDJTpNU5JSaF58+Y8fPiQffv2cfHiRSZNmiRTJ0W9fPmSvn37cuLECU6fPo2NjQ1eXl7v9OP+/v37dO3aFW9vbyIjIxk0aBBTpkyRSXPr1i08PT3p1q0bUVFRBAUFceLECUaNGlXiOKmpqWzevJnq1atjbm7+xrRpaWksWbKErVu3cuzYMWJjY2XO7/scd2ZmJl999RWRkZEcP36catWqlfgY3iYpqaDXx8DAACjo5c3OzqZNmzbSNPb29lSrVk16Q6e4fArzKLR582Zu376Nv7+/XPqsrCzOnz8vE0dJSYk2bdoUG0fR+QgPD6dWrVqYmJhI03l4eJCcnMyVK1cU5rNx40batGmDhYWFdN3r17Wvry87d+4kPz9fui4oKAgzMzOaNm0ql+euXbtYvnw5P/74Izdu3GDv3r3UqlVLYfy8vDw6derEixcvCAsL4/Dhw9y+fZsePXrIpLt16xZ79+5l//797N+/n7CwMBYsWKAwz/JMRVmCo1lFTt961Qubnw+nb72gtnnJHvPoWs+Mg5fiSM9W/L1VVHZ2NrdionGu11C6TklJidr1GhJztXR+fGZnZxN2+CCtvTqRk5PD89ibmDm4SLdLlJQwc3Dh6RuGeUb++T80K+ph19jjrfFyc7KJOX4QNU1tDKpWf7eyZmURffUKjdzcpeuUlJRo1MidqIsR75TXx5Sdnc31mKvUrd9Iuk5JSYm69Rtx9dLF984/NyebuDvXsXCqK10nUVLC0qkuD29efev++fn53L18gRdxDzC3f/XdUsXGkZsXwnn54jn5+fncuxpJQtwD3Bo3KXHZyvocfqh4uTnZPL13A3NH2To2d6zD41vF1/E/+7ajWVEPp2ae/zq2IBRHzA4t/Cd069ZN5v+bNm3C2NiYq1evUqFCwfNiEyZMoEOHDgDMnj0bJycnbt68ib29Pbq6ukgkEkxNTeXy9vLyYsSIEQBMnjyZ5cuXExoaip2dnTTN2LFjZYalLlmyhMmTJ9OzZ08AFi5cSGhoKAEBAaxZs0aabtSoUdKyr1u3juDgYDZu3MikSZPkyrFo0SJcXV1Zu3atdF3RZyd1dXVlyrRjxw6ePXvG2bNnpY0ga2vrYuuwVatWMv9fv349enp6hIWF0bFjx2L3K2rdunVYWVmxdOlSAOzs7Lh06RILFy6Uppk/fz6+vr6MHTsWABsbG1auXEnz5s1Zt24dGhoaxea/du1aJk2aRGpqKnZ2dhw+fBg1tTff5c/OzuaHH37AysoKKKjzOXNePYP3b487JSWFDh06kJmZSWhoaKk+u52Xl8fYsWNp3LgxNWsWDAGLi4tDTU1N7tl1ExMT4uLiFOZz6tQpgoKC+PPPP6Xrbty4wZQpUzh+/DgqKvJ/Ap4/f05ubq5M47UwzuvP/L7pfMTFxSnMo3Db6x49esTBgwfZsWOHzPrXr+vu3bszduxY6WgCKLjWfXx8FD46EBsbi6mpKW3atEFVVZVq1arRoEED+coCjhw5wqVLl7hz5460Mf/zzz/j5OTE2bNnqV+/PlBwfgIDA6lYsSIAvXv35siRI3z//fcK8y2v9LXUUFFWIj4lS2Z9fEoW1Y2037p/zSo62JpWZOaetzdEAF4mJZKXl4veazd9dPUNeBB7t8TlfpMzJ0JJTXlJa88veJmUSH5eHpo6+jJpNCvqkRR3X+H+cTevEHPyEF1myD+iUFRs1BlCNy4kJysTLR0DPMd8j0aFd/uOSUhMIDc3F0NDQ5n1hoaG3Llz+53y+piSEhPIy81F30D2OPT1Dbl/9917x1+X9jKJ/Lw8tHVlz6OWrj7xjxWfR4DMtFTWjO5Jbk42EiUl2vXzo3qtetLtbfqM5NDGANb6+aCkrIxEooTnwG+o51q/xGUr63P4oeKlv0wmPy8PLR09mfVaOvokFFPHj65f5srxQ/SatVbhduED+IyGMpeG8nW0wn/WjRs38PHxoUaNGujo6EiHnsbGxkrTODu/moigcuWCYTVFn3EsTtH9ChvKr+/n6uoq/XdycjKPHj2icePGMmkaN25MdHS0zLqiQ1FVVFRwdXWVS1OosCe4OF26dJFppERGRlKnTh25XsDiPHnyhMGDB2NjY4Ouri46OjqkpKTI1OHbREdH07BhQ5l1rw+3vXjxIoGBgVSoUEG6eHh4kJeXx507d5g3b57MtqLxfX19iYiIICwsDFtbW7p3705GRgZQcEOgcJ+ik1hpaWlJG8BQcO6Lnr9/e9w+Pj6kpqYSEhLyTg3g48ePyxzf9u3yE/GMHDmSy5cvs3PnzhLn+7rLly/TqVMn/P39adeuHVAwdLtXr17Mnj0bW1vbf513oTedj3e1ZcsW9PT06Ny5s8z6169rY2Nj2rVrJ623O3fuEB4ejq+vr8J8v/rqK9LT06lRowaDBw9mz549MpNzFRUdHY25ubnM6AJHR0f09PRkPpeWlpbSBjDIX1Ovy8zMJDk5WWbJz/vwz7J/7rq6VuF63EsuP0z+2EWR+uvAXuo2dMfAyPid983KSCNs8xKafO331gZtZbvadJm+Gu+JS6nqVI+/N8x/63PGQtlS09Ck//c/0GfOapp91Z+/t/9A7NVXPdPnQ37n0c1ouo2bQ9+5a2nZawiHt6zidPipj1jqz0NWehohPy2idd+xaFYUk4OWmXL2TLDoCRb+E7y9vbGwsGDDhg2YmZmRl5dHzZo1pc8eAqgWed6rsMeouKHBRam+9pyYRCKR209b++29Gu9LU1Pzg6bv27cv8fHxrFixAgsLC9TV1XFzc5PWoZJSwRdb0WGo/2YSsJSUFIYOHSrzXGahatWqMWzYMLp37y5dZ2ZmJv23rq4uurq62NjY0KhRI/T19dmzZw8+Pj4cOHBAWp6ix67o/BU9hrcdd3G8vLzYtm0b4eHhMr3JSkpKMvmDbD25urrKPFf9em/pqFGj2L9/P8eOHaNq1arS9aampmRlZZGYmCjTG/zkyRO5EQxXr16ldevWDBkyhBkzZkjXv3z5knPnzhERESEdfp6Xl0d+fj4qKiqEhITQpEkTlJWVZZ6DLS7Om86Hqamp3IzShXm+nk9+fj6bNm2id+/eb+3Zh4LGt5+fH6tWrWLHjh3UqlWr2CHO5ubmxMTE8Ndff3H48GFGjBjB4sWLCQsLk7s2Sqok3wlFzZ8/n9mzZ8usUzapj2plxT3S/xUJaVnk5OZhWEH2nBpWUON5SuYb99VUVaJ9LRPWHLlV4ngVdfVQUlIm8YXsJFhJCS/kehH/jadxj4g6/w+T5yyRxpMoKZGeLDsvQ/rLRDR15G8+vnz2mJT4Jxxe++paKPyu2DSiI1/O3oDO/z/3qKqugWolM3QqmVGphj2/fjuI66cOQYd6cvkWR19PH2VlZblHbOLj4zEyevtkUZ8KXT19lJSV5SbBSkiIx8Dw/c+rVkVdJEpKcpNgpSUlyPUOFyVRUkLftODZdhMLa+IfxhL+x/+o5lib7KxMjv2yia5jZ2FVp+DGcKVqNXh67xZbNm+UGW78JmV9Dj9UPM2KOkiUlOQmwUpLTkBLQR0nPXtM8vMn/LHy1USKhZ+VVYPa03veRvQqmcntJwjv4vNprgtCMeLj44mJiWHGjBm0bt0aBweHN04WpYiamlqpzTKso6ODmZkZJ0+elFl/8uRJHB0dZdYVTmgEkJOTw/nz53FwcFCYr7OzM0eOlHxWSWdnZyIjI3nxQvGsqK87efIkfn5+eHl54eTkhLq6Os+fP5duNzYu6PkoOqnU6+/KdXBwkGv4FD1GgLp163L16lWsra3lFjU1NQwMDGTWKRqyCwV/EPPz88nMLPgxbWFhId2nShXFk+78m+MuzvDhw1mwYAFffPGFzIRmxsbGxMXFyTSEi9aTpqamzPEV9ijm5+czatQo9uzZw99//0316rLP/9WrVw9VVVWZayAmJobY2FiZ3vYrV67QsmVL+vbtKzc8V0dHh0uXLhEZGSldhg0bhp2dHZGRkTRs2BA1NTXq1asnEycvL48jR44onESr0Ovnw83NjUuXLsn0kB4+fBgdHR25z0FYWBg3b95k4MCBxeZfVKdOncjIyCA4OJgdO3YU2wtcSFNTE29vb1auXMnRo0cJDw/n0iX516s4ODhw//597t9/NTzv6tWrJCYmypX5XUydOpWkpCSZRcWk5I2Zz1VObj5XH72kYY1XDUKJBBrWMODi/TfPdNyupglqykr8Eal4qL8iqqqqWNk5EHXh1XdQXl4eUef/wc7x/V9JcuTgPnT1DHBt1EQaz6iaNY+vver9y8/L49G1SCrVsJfbX9fUnC7frqXz9NXSpZpzQyrbOtN5+mq09YtvZOTn55H7jjcdVdXUcHB04szpV8/y5+XlceZMOM6167xTXh+TqqoqtnaORJx9NaljXl4eEWdP41ir9nvnr6yiiml1W+5defXMa35eHnevRFDFuuSf+/z8fOk5ysvJIS83B5RkH9GQKCmT99pN0jcp63P4oeIpq6hSycKG+9GydXw/OpLKVvJ1rF/ZHN85P9Jr1jrpUsOlEVXta9Nr1joqGrz7SAyhBMrZxFiiJ1j47Onr62NoaMj69eupXLkysbGxcpMxvY2lpSUpKSkcOXKE2rVro6Wl9V6vTZk4cSL+/v5YWVnh4uLC5s2biYyMlBv6umbNGmxsbHBwcGD58uUkJCQwYMAAhXlOnTqVWrVqMWLECIYNG4aamhqhoaF89dVXGBkZsWfPHqZOnSodOurj48O8efPo3Lkz8+fPp3LlykRERGBmZqawMWNjY8PWrVtxdXUlOTmZiRMnyvSoampq0qhRIxYsWED16tV5+vSpTC8jFMw4vHTpUiZOnMigQYM4f/48gYGBMmkmT55Mo0aNGDVqFIMGDUJbW5urV69y+PBhha/yAbh9+zZBQUG0a9cOY2NjHjx4wIIFC9DU1MTLy+ut5+NN3nbcbzJ69Ghyc3Pp2LEjBw8epEmTJrRo0YJnz56xaNEivvzyS4KDgzl48CA6OsW/GxUKhkDv2LGD33//nYoVK0qfm9XV1UVTUxNdXV0GDhzIuHHjMDAwQEdHh9GjR+Pm5kajRgUTxly+fJlWrVrh4eHBuHHjpHkoKytjbGyMkpKS9BnjQpUqVZLOTl5o3Lhx9O3bF1dXVxo0aEBAQACpqanSWZtLcj7atWuHo6MjvXv3ZtGiRcTFxTFjxgxGjhwp97qpjRs30rBhQ7myAXLXNRSMvOjcuTPffvst0dHR+Pj4FFuvgYGB5Obm0rBhQ7S0tNi2bRuampoyk28VatOmDbVq1cLX15eAgABycnIYMWIEzZs3l3nk4V2pq6vLHbNESfmd8tDWVMPK/NUPP8sqhjjbViEhOY37ce92068s/XzyHt93c+LKo2QuP0jma/dqaKops/f8IwDmdXPiaXImAYdlX3fUtV4V/o5+RlK6fMNPR1OFyroaVNIpmD+g8PliZSUJnb7yZcV8f6ztHLFxcOKP33aQkZFO6/ZfABAw71sMjSrRe8hooGCUxv27Bc865uRk8+L5U27fiEFTU5PKVV9NdJeXl8ffwfto6dER5SI35mq26cKxwGUYWdhgbGnL5b9/JycrE1v3tgCEbV6Clp4h9bv0R0VVDYMqljLHoq5ZMGdF4frszAwuHtxJNedGaOrqk5mSzNWw/aQlxlO9nvzEb2/Tu29/vp02GSenmtSs5cy2rVtIT0+nc5eub9/5X/hQ1+mXPn1YOHc6tg5O2DvWYlfQVjIy0vHo0BmABbOnYWRciUEjxgIF5/XenYJRBDk52Tx/9pSb16+hqalFFXP5CQzrt+/Gnz8uwrS6LZWt7DgXvIfszAxqNS+YvGz/DwupqG9E8x4FN+rC9/0P0+q26JuYkZOdxe2L/3Dl5F+061cwwkldSxtze2eO/m8Dqqrq6BhV4v61KK6cOMzkKVPf6djL+hx+qHh1PLpy+KclmFjaYlLdjsjDe8jJzMCxScHjOiEbFqGtb0TjLwegoqqGYVVLmf3VtQo+K6+v/xx8Nt/fn9FQ5tIgGsHCZ09JSYmdO3fi5+dHzZo1sbOzY+XKlbRo0aLEebi7uzNs2DB69OhBfHw8/v7+Mq9Jeld+fn4kJSUxfvx4nj59iqOjI/v27cPGxkYm3YIFC1iwYAGRkZFYW1uzb9++Yocc2draEhISwrRp02jQoAGampo0bNhQ2gBISkqSmbVaTU2NkJAQxo8fj5eXFzk5OTg6OspMzFXUxo0bGTJkCHXr1sXc3Jx58+bJzKIMBROODRw4kHr16mFnZ8eiRYukz5tCwXDmXbt28c0337Bq1SoaNGjAvHnzZBr2zs7OhIWFMX36dJo2bUp+fj5WVlZyM/AWpaGhwfHjxwkICCAhIQETExOaNWvGqVOnqFSpUrH7lURJjvtNxo4dS15eHl5eXgQHB+Pu7s7atWuZN28ec+fOpVu3bkyYMEHm1VqKrFtX8O7R16/bzZs3069fPwCWL1+OkpIS3bp1IzMzEw8PD5mJ0n777TeePXvGtm3b2LZtm3S9hYVFiV4lVahHjx48e/aMmTNnEhcXJ33FV+HQ7ZKcD2VlZfbv38/w4cNxc3NDW1ubvn37ykxKBgXX7a5du1ixYoXCsrx+XRfy9fXFy8uLZs2avXFWbj09PRYsWMC4cePIzc2lVq1a/PHHH3ITv0DBsObff/+d0aNH06xZM5SUlPD09GTVqlUlrrsPpa6jBSE/jZH+f9GEggn1tu47zRD/bcXt9tEFX36CvrYao1pbYVRBnWuPXzJsywXiUwseN6isp0Heax1jlkZa1LPUZ/Dm8wrzbGlvzPfdXt0wWdKzoJf32cssmrTyICkxgf9tXkfCi3iqW9vhv2g1ev8/HPrZkzgkRX7ovXj+jHGDX91E2Ru0lb1BW3GqXY/vV2yQrr94/gzPnsTR2quTTFlquDYn42Uy5//YSnpyAoZVa+Axeo50sqyUF89k4r2NREmJxLgH3Aj/nozUJDS0dTCysKXDhMXom8nfuHkbz/ZeJLx4wdrVK3n+/Bl29g6s/fEnDD/QcOgPdZ22bOtJUuILAjesISH+OVY29ixY/oP0HcBP4x7LTIwX/+wpQ/u8et/4L9sD+WV7ILXruLJs3Wa5/B0atSAtOZETu7aQmpRAJQsruk+aJx0Onfz8qUz+2ZkZHA5cycsXz1FRU8fAzJyOw6fg0KiFNM0Xo6YTFrSRP9bNJyPlJTpGJjT9qj9f9Sj+pp0iZX0OP1Q82wYtSH+ZxOm9P5OalICxeQ06ffO9dDj0yxfPkCj9Nxthn83392fUi1saJPmvP7wmCMIHd/fuXapXr05ERITcO2UFQfjv06xT8leClZb0iDfPSFzaas44XKbxfh1ZsucsS9Mf10o+XLs0+DW1enuiUqRfv+yv0xt/LyvTeCE3y/Yc9qpTeq/S+1RtPHO3TOONbGxZpvGg7L/Dy+L7W7Pzm2/Wl1T63iGlks+HJnqCBUEQBEEQBEEQyjMxHFoQBEEQBEEQBEEoN8rZcGjRCBaEj8DS0lLuNTqCIAiCIAiCIHx4ohEsCIIgCIIgCIJQjklET7AgCIIgCIIgCIJQXpS3RnD5egJaEARBEARBEARBKNdET7AgCIIgCIIgCEJ5Vr46gkUjWBAEQRAEQRAEoTwrb8OhJfliilpBEARBKFMzD90o85hNq+mXaby2DkZlGi/2RWaZxgOopKNe5jHLUnJ6dpnH1NFULfOYgvCuhv0aVabxAn2cP3iMCt0DSyWflF/6lUo+H5roCRYEQRAEQRAEQSjHyltPsGgEC4IgCIIgCIIglGOiESwIgiAIgiAIgiCUG+WtESxekSQIgiAIgiAIgiCUG6InWBAEQRAEQRAEoTwrXx3BohEsCIIgCIIgCIJQnonh0IIgFMvS0pKAgICPEvvo0aNIJBISExM/SP6zZs3CxcXlg+Rd1Ic+DkEQBEEQBEF4E9EIFgQFAgMD0dPT+9jFkOHu7s7jx4/R1dX92EX5JAwdOhQrKys0NTUxNjamU6dOXLt27WMXq1S0aNGCsWPHvjXd0aNH6dSpE5UrV0ZbWxsXFxe2b98ul+7XX3/F3t4eDQ0NatWqxYEDB6TbsrOzmTx5MrVq1UJbWxszMzP69OnDo0ePFMbMzMzExcUFiURCZGSkzLaoqCiaNm2KhoYG5ubmLFq0SGb7hg0baNq0Kfr6+ujr69OmTRv++eefdy7LhQsXaNu2LXp6ehgaGjJkyBBSUlLeWFf9+vWjc+fOb0zzb7xvvjeO7eePWQP4dVwXDi8dR/y9mBLtF3s+jCC/jpzY8J3M+jPblhPk11FmCVs7U7o97MAuZg7uxtivWrJ44mDuXr9abIyTIftYPnU4E309mejryaqZY+TSJye+YOuK75jW/wu+6d6KNbPH8fTR/XeogdL3+287+bqLJ17NXRk9sBfXrlwqNu3d2zeZPfUbvu7iSVs3Z3bv3CqXJiriHN9OGEUP79a0dXPmZNjf/7psO3dsp33bVtSvUwvfnl9xKerDv2v038Qs3Ke1e12G9PXh6uXi6xAg9K9D+HbzprV7Xfr26EL4iWMy25u61lS47Ph5kzTNzxt/ZPgAXxrWq02TRq4f9PjeV1nH/K/H+xgxSyNeaxtDlnjbs6F7Tb5ta011A81i0zaprk+gj7PMsqF7zWLT93WtQqCPM+3syu596xKJpFSWz4VoBAvCB5adnV0q+aipqWFqavpZfcF8SPXq1WPz5s1ER0dz6NAh8vPzadeuHbm5uR+7aGXm1KlTODs7s2vXLqKioujfvz99+vRh//79Mml8fHwYOHAgERERdO7cmc6dO3P58mUA0tLSuHDhAt9++y0XLlxg9+7dxMTE8MUXXyiMOWnSJMzMzOTWJycn065dOywsLDh//jyLFy9m1qxZrF+/Xprm6NGj+Pj4EBoaSnh4OObm5rRr146HDx+WuCyPHj2iTZs2WFtbc+bMGYKDg7ly5Qr9+vUrjSotU7EXjhG55yecPH1oN3EFelWqE7Z2JhkvE9+4X2r8EyL3bsLYyknhdlOHenzx3Vbp4tZvEgDnT/zFnk2raN9zAJOXbaKKpTVrZo/jZWKCwnxuXL5AvaZtGTN3JeMX/oieUSXWzPqGxPhnAOTn57N+/hSeP3nE0GkLmbJ8MwbGpqzyH0NmRvq/r5j3cPSvYH5cuZivBw5jXWAQNWzsmPrNMBJexCtMn5mRQWWzqgwcMQYDQ8U/NjMy0qlhY8fo8dPeq2zBBw+wZNF8ho4Yyc5f92BnZ8/woQOJj1dcttLwb2IW3eenbb9ibWvH+NFDi63DSxcjmD19Eh06dWHj9l9p2qIV0yb4cfvmDWmavcFHZZYpM+cikUho0aqtNE12TjYtWnvwVQ+fD3p876usY/7X432MmKURr0E1XXrWqczey0/wD77B/cR0JrSsTkV15WL3ScvKZcyeq9Jl/D7FN+7rVtXBykiLhLTS+f1YUqIRLAj/AcHBwTRp0kTaU9SxY0du3boFwN27d5FIJOzevZuWLVuipaVF7dq1CQ8PBwp+qPfv35+kpCTpB3rWrFnSvNPS0hgwYAAVK1akWrVqMj/yC/MOCgqiefPmaGhosH37dvLy8pgzZw5Vq1ZFXV0dFxcXgoOD5fbbuXMn7u7uaGhoULNmTcLCwqRpFA0jPnnyJC1atEBLSwt9fX08PDxISFD8gxbgwYMH+Pj4YGBggLa2Nq6urpw5c0Zh2rNnz9K2bVuMjIzQ1dWlefPmXLhwQa7MRXsDExMTkUgkHD16VLruwIED2NraoqmpScuWLbl7965crBMnTtC0aVM0NTUxNzfHz8+P1NTUYo8DYMiQITRr1gxLS0vq1q3Ld999x/379xXmX+jevXt4e3ujr6+PtrY2Tk5OMr2iYWFhNGjQAHV1dSpXrsyUKVPIycmRbm/RogV+fn5MmjQJAwMDTE1NZa4NKPgj8tNPP9GlSxe0tLSwsbFh3759MmkuX75M+/btqVChAiYmJvTu3Zvnz58DBT2LYWFhrFixQnr9FXdM06ZNY+7cubi7u2NlZcWYMWPw9PRk9+7d0jQrVqzA09OTiRMn4uDgwNy5c6lbty6rV68GQFdXl8OHD9O9e3fs7Oxo1KgRq1ev5vz588TGxsrEO3jwICEhISxZskSuLNu3bycrK4tNmzbh5OREz5498fPzY9myZTJpRowYgYuLC/b29vz000/k5eVx5MiREpdl//79qKqqsmbNGuzs7Khfvz4//PADu3bt4ubNmwrradasWWzZsoXff/9dWqeF1+j9+/fp3r07enp6GBgY0KlTJ2l9X7t2DS0tLXbs2CHN65dffkFTU5OrV6++Md+SiAndSw13D2o0aotu5Wq4dh+Jipo6d04fLnafvLxcwn9eQk0vX7QNTRWmUVZRRVNHX7qoaVUA4O/fg3Bv541b6w5UNq9Oz+ETUVNXJ/zIfoX59Bs3i2ZeXalawxbTqhb4jpxCfn4eMVHnAHj66D53Y67Qc9gELGwcMKliQY9hE8jOyuT88eKP4UPa9b+faf9FNzw7dsaiuhVjJn2Luromh/bvVZjezrEmQ0aPp2Xb9qiqqilM08CtKf2HjqZJi9bvVbatWzbT9cvudO7SDStra2b4z0ZDQ4O9u3e9V76lHbPoPtVrWDFh6kw0NDT4c98ehel/27mNBm6N6dVnAJbVrRg0fDS29o7s/uXV58bQyEhmOREWSh3XBphVNZemGTh0FD18+2BjY/tBj+99lXXM/3q8jxGzNOJ52BkTdusFJ+4k8Cg5ky1nH5KVk0+zGgZv3C8pI0e6JGfkyG3X01Th63pm/HAqlty8/Hc+NqHkRCNY+E9KTU1l3LhxnDt3jiNHjqCkpESXLl3Iy8uTppk+fToTJkwgMjISW1tbfHx8yMnJwd3dnYCAAHR0dHj8+DGPHz9mwoQJ0v2WLl2Kq6srERERjBgxguHDhxMTIzuEccqUKYwZM4bo6Gg8PDxYsWIFS5cuZcmSJURFReHh4cEXX3zBjRs3ZPabOHEi48ePJyIiAjc3N7y9vYu9MxkZGUnr1q1xdHQkPDycEydO4O3tLe0JDQwMlLkjl5KSQvPmzXn48CH79u3j4sWLTJo0SaZOinr58iV9+/blxIkTnD59GhsbG7y8vHj58mWJz8P9+/fp2rUr3t7eREZGMmjQIKZMmSKT5tatW3h6etKtWzeioqIICgrixIkTjBo1qsRxUlNT2bx5M9WrV8fc3LzYdCNHjiQzM5Njx45x6dIlFi5cSIUKBQ2Ehw8f4uXlRf369bl48SLr1q1j48aNfPed7HDTLVu2oK2tzZkzZ1i0aBFz5szh8GHZH/yzZ8+me/fuREVF4eXlha+vLy9evAAKbhS0atWKOnXqcO7cOYKDg3ny5Andu3cHChqtbm5uDB48WHr9vemYXpeUlISBwas/wuHh4bRp00YmjYeHh/SmT3F5SCQSmUcCnjx5wuDBg9m6dStaWlpy+4SHh9OsWTPU1F41JDw8PIiJiSn2xkxaWhrZ2dky5X1bWTIzM1FTU0NJ6dWfL03NgiFoJ06ckK6ztLSU3qCYMGEC3bt3x9PTU1qn7u7uZGdn4+HhQcWKFTl+/DgnT56kQoUKeHp6kpWVhb29PUuWLGHEiBHExsby4MEDhg0bxsKFC3F0dCw235LIzckm4f5NTOxcpOskSkqY2Lnw/E7xw/qvBu9Eo6IuNdzaFZvm6c1L7J3my4HvhnIuaA2Zqcnk5mRz/1YMds71pemUlJSwq+3KnZjLJSpzVlYGubk5aFXQASDn/0e5qBRpPCopKaGiosatqx9++OTrsrOzuR4TTd36jWTKU7d+Q65evljm5SkqOyuL6KtXaOT26vpQUlKiUSN3oi5GfDIxi9vHtUEjrkQprsPLURdxbeAms66BmzuXLylO/yL+OeEnjtGxU9d3PaQSlfVTq1MR79OKWVrxLA00uRr36jGcfODKk5dYGcn/fSykrqLEki/sWfqFPX5NLTDTUZfZLgGGuFXjYPQzHiVnlrgspaW89QSL2aGF/6Ru3brJ/H/Tpk0YGxtz9epVaaNnwoQJdOjQAShotDg5OXHz5k3s7e3R1dVFIpFgairf0+Ll5cWIESMAmDx5MsuXLyc0NBQ7OztpmrFjx9K166s/8EuWLGHy5Mn07NkTgIULFxIaGkpAQABr1qyRphs1apS07OvWrSM4OJiNGzcyadIkuXIsWrQIV1dX1q5dK13n5PRqeKSurq5MmXbs2MGzZ884e/astNFhbW1dbB22atVK5v/r169HT0+PsLAwOnbsWOx+Ra1btw4rKyuWLl0KgJ2dnbTxWWj+/Pn4+vpKn4G1sbFh5cqVNG/enHXr1qGhoVFs/mvXrmXSpEmkpqZiZ2fH4cOHZRphr4uNjaVbt27UqlULgBo1asjkZW5uzurVq5FIJNjb2/Po0SMmT57MzJkzpY0uZ2dn/P39pWVdvXo1R44coW3bV8P6+vXrh49PwZC+efPmsXLlSv755x88PT1ZvXo1derUYd68edL0mzZtwtzcnOvXr2Nra4uamhpaWloKr783+eWXXzh79iw//vijdF1cXBwmJiYy6UxMTIiLi1OYR0ZGBpMnT8bHxwcdnYLGTn5+Pv369WPYsGG4uroq7JmOi4ujevXqcnEKt+nr68vtM3nyZMzMzOQa6W8qS6tWrRg3bhyLFy9mzJgxpKamSm+sPH78WLqvlZUVRkYFw1srVKiApqYmmZmZMnW6bds28vLy+Omnn6R/uDdv3oyenh5Hjx6lXbt2jBgxggMHDvD111+jpqZG/fr1GT169BvzfV1mZiaZmbI/aNIS4snPy0Ojop7Meo2KeiQ/eaAwn2e3rnA7PASPySuLjVXZoS5Va7ujbWhCyvPHXPrjZ46t88d94FTy8nKpqCd7w0FH14AnD2KLyU3W71vWoatvhH3tguc1TataoG9swr6tP+IzYiJq6pqE/hFEYvxTkhI+3NDJ4iQlJpCXm4u+gaHMen0DQ+7fu1Pm5SkqITGB3NxcDA1ly2ZoaMidO7c/mZjF7aNvYMi9u4rr8EX8cwxeq3MDAyNexD9XmP7g/n1oaWvRrKXiz31JfS51KuJ9WjFLK56ykoSk13pykzNyqFxR8W+Wx8mZbDxznweJGWiqKtPewZgZba2ZfuA6CekFNxS9HI3Jy8vn8PWy//4Eyt0rkkRPsPCfdOPGDXx8fKhRowY6OjpYWloCyAzvdHZ2lv67cuXKADx9+vSteRfdr7Ch/Pp+rq6vJvVITk7m0aNHNG7cWCZN48aNiY6Ollnn5vbqbrqKigqurq5yaQoV9gQXp0uXLjITRUVGRlKnTp039roVVdjzZ2Njg66uLjo6OqSkpMgNkX2T6OhoGjZsKLOu6DECXLx4kcDAQCpUqCBdPDw8yMvL486dO8ybN09mW9H4vr6+REREEBYWhq2tLd27dycjIwMouCFQuE/79u0B8PPz47vvvqNx48b4+/sTVWQijOjoaNzc3GTuYjZu3JiUlBQePHjVKCl6/qHg2nn9/BdNo62tjY6OjjTNxYsXCQ0NlTkme3t7AOmQfUUUHU9RoaGh9O/fnw0bNsjcDHkX2dnZdO/enfz8fNatWyddv2rVKl6+fMnUqVP/Vb6KLFiwgJ07d7Jnzx6FNzqKK4uTkxNbtmxh6dKl0hsF1atXx8TERKZ3+MiRI28dTXDx4kVu3rxJxYoVpXVrYGBARkaGzLnYtGkTUVFRXLhwQW6ERUnMnz8fXV1dmeXsvp/fKY/sjDTObF1GfZ/RqFcofnK8avWaU6VWQ/TMLKnq7EbTof68iL1B/Bt6l0siZNdWzp/4i8FT56OqVtB7oayiwuDJ83j6KJZJX7dnXI/WXL90Ace6jWTOhSAUdWDfHtp6dkRdXf3tiQXhP+JWfBqn7iYSm5hBzLNUVh2/y8vMHFpaF/wms9DXpJ2tET+d+bgTC5YnoidY+E/y9vbGwsKCDRs2YGZmRl5eHjVr1iQrK0uaRlVVVfrvwh+1xQ0NLqrofoX7vr6ftrb2+xS/RAqHgH6o9H379iU+Pp4VK1ZgYWGBuro6bm5u0jos/JGbn//qmZV/MwlYSkoKQ4cOxc/PT25btWrVGDZsmHSoMCAzKVNhg8LGxoZGjRqhr6/Pnj178PHx4cCBA9LyFB77oEGD8PDw4M8//yQkJIT58+ezdOlSac9eSZTk/L8pTUpKCt7e3jK94YUKb8Yoouh4CoWFheHt7c3y5cvp06ePzDZTU1OePHkis+7JkydyPZeFjc579+7x999/S3teAf7++2/Cw8PlfrS6urri6+vLli1bio1TWIailixZwoIFC/jrr7/kbiq8rSwAvXr1olevXjx58gRtbW0kEgnLli2T6dkviZSUFOrVq6dwRm1jY2Ppvy9evEhqaipKSko8fvz4jedJkalTpzJu3DiZdfOO3GbvpRNyk2BlvExEo6J8r3nK8zhSXzzh+Po50nWFn71fxn6B1/QfqWAsX64KRqaoa+uQkZKMkpIyLxNfyGxPTnqBjv6bb4z9tXcHh3dtY9ScAKpYyo4eqWZtz9SALaSnppCTk01FXX0WTxxMNWv7N+b5Iejq6aOkrCw3gVPCi3j0i5n0qqzo6+mjrKws93hLfHy8dMTCpxCzuH0SXsRjWEwdGhga8eK1On/x4rnCicYuRpwn9t4dZs9f/C6H8k5l/dTqVMT7tGKWVrzcvHx0NWSbUToaKiRllOx3UG4+xCakU6liwd9Vu0raVNRQYekXDtI0ykoSerpUpp1t2Xx/fU5DmUuDuFUr/OfEx8cTExPDjBkzaN26NQ4ODm+cLEoRNTW1UptlWEdHBzMzM06ePCmz/uTJkzg6OsqsO336tPTfOTk5nD9/HgcHBxRxdnaWTihUEs7OzkRGRkqfTX2bkydP4ufnh5eXF05OTqirq0snb4JXjYSiQ1Bff2WOg4ODzCtwQPYYAerWrcvVq1extraWW9TU1DAwMJBZp6Ki+N5dfn4++fn50mGnFhYW0n2qVKkiTWdubs6wYcPYvXs348ePZ8OGDdKyhoeHyzTqT548ScWKFalatWqJ6qwk6taty5UrV7C0tJQ73sKbJ4quv+KO5+jRo3To0IGFCxcyZMgQuXhubm5y18nhw4dleuQLG503btzgr7/+khsmtnLlSi5evEhkZCSRkZHSycSCgoL4/vvvpXGOHTsmcyPk8OHD2NnZyQyFXrRoEXPnziU4OFhmxERJy1KUiYkJFSpUICgoCA0NDZkh6a9TVKd169blxo0bVKpUSe5cFL6K7MWLF/Tr14/p06fTr18/fH19SU9Pf2O+r1NXV0dHR0dmUdPSRt/cmifXXz03mZ+Xx5OYixhVl29A6phUxWPKatpNWildqtRsSCWbWrSbtBJNfcU/ktISnpOZ9hJtfWPMreykk1pBwU2/61HnqW5X/Gs6Du/eTvAvgYzwX4qFteLvIgBN7QpU1NXn6aP7xN66hnODJm+skw9BVVUVWzsHIs69muwvLy+PiHNncKxZu8zLU5SqmhoOjk6cOf3qWfy8vDzOnAnHuXadTyZmcfucP3sGJ2fFdVjTuTbnz8p+r587E07NWvLp9/++GzsHR6xt3/8myedSpyLepxWztOLdfZGOo2kF6f8lgKNJBW49TyvR/hIJVNXTIPH/h0KfvJPAtwevMzP41ZKQls3Ba89YcrRsHucob88Ei0aw8J+jr6+PoaEh69ev5+bNm/z9999yvTBvY2lpSUpKCkeOHOH58+ekpZXsS604EydOZOHChQQFBRETE8OUKVOIjIxkzJgxMunWrFnDnj17uHbtGiNHjiQhIYEBAwYozHPq1KmcPXuWESNGEBUVxbVr11i3bp20obpnzx7pMFsAHx8fTE1N6dy5MydPnuT27dvs2rWr2AmSbGxs2Lp1K9HR0Zw5cwZfX1+ZHkhNTU0aNWrEggULiI6OJiwsjBkzZsjkMWzYMG7cuMHEiROJiYlhx44dBAYGyqSZPHkyp06dYtSoUURGRnLjxg1+//33Nw5lvX37NvPnz5fOGnzq1Cm++uorNDU18fLyKna/sWPHcujQIe7cucOFCxcIDQ2V3mQYMWIE9+/fZ/To0Vy7do3ff/8df39/xo0bV6pDO0eOHMmLFy/w8fHh7Nmz3Lp1i0OHDtG/f39pY8rS0pIzZ85w9+5dnj9/XuwIhdDQUDp06ICfnx/dunUjLi6OuLg4mRsdY8aMITg4mKVLl3Lt2jVmzZrFuXPnpPWbnZ3Nl19+yblz59i+fTu5ubnSfAp7/atVq0bNmjWli61tweytVlZW0hsEvXr1Qk1NjYEDB3LlyhWCgoJYsWKFzGdv4cKFfPvtt2zatAlLS0tpnMJ3/JakLACrV6/mwoULXL9+nTVr1jBq1Cjmz58vM5FX69atpTNgF9ZpVFQUMTExPH/+nOzsbHx9fTEyMqJTp04cP36cO3fucPToUfz8/KRD4IcNG4a5uTkzZsxg2bJl5ObmykyUpyjfkrJr2Znbpw5x58wRkuPuc+6XteRkZVC9YcGzkqe3LiVqXyAAyqpq6JlZyiyqmtqoqmuhZ2aJsooq2ZnpRO7dxPM710iNf8KTmEhObJhLBaPKmNrXpVWnHpw6/Aen/z5A3P27BP2whMyMDBq1Lpgb4eeAufy+9dXQ88O7t/Hnjg34jpqKYaXKJCfEk5wQT2b6q+/DCyf/5vqlCzyPe0jUmeOs9h+Lc4OmONSRfQyirHTz6cOBfbsI+fN37t29zcpF35GRkY5Hx84ALJw9jY1rV0jTZ2dnc/P6NW5ev0Z2TjbPnz3l5vVrPLz/6rGL9LQ0aRqAuEcPuXn9Gk/jHvMuevftz+7ffmHf3j3cvnWL7+bMIj09nc5d3m+CqNKOWXSfu3dusXT+XNLT0/Hy7gzAdzOn8sPq5dL0X/b8mjOnTrJzWyD37t5m049ruHb1Cl2795LJNzUlhaN/hdCxk+ycHYWexD3mRsw1Hj9+RG5uLteio7kWHU3aG94U8LnUqYj3acUsjXiHYp7R3MqAxtX1qayjTp/6VVBXUeL4nYJOl8GNzPmy9qtRUF84VcLJtALG2mpY6Gsy1K0ahlpqHLtV8Pc6NSuXh0mZMktuXj5JGTnEvSybSbLKWyNYDIcW/nOUlJTYuXMnfn5+1KxZEzs7O1auXEmLFi1KnIe7uzvDhg2jR48exMfH4+/vL/cqnHfh5+dHUlIS48eP5+nTpzg6OrJv3z5sbGxk0i1YsIAFCxYQGRmJtbU1+/btK3Z4jq2tLSEhIUybNo0GDRqgqalJw4YNpRMyJSUlycxaraamRkhICOPHj8fLy4ucnBwcHR1lJuYqauPGjQwZMoS6detibm7OvHnzZH78Q8GzkgMHDqRevXrY2dmxaNEi2rV7NWtttWrV2LVrF9988w2rVq2iQYMGzJs3T6Zh7+zsTFhYGNOnT6dp06bk5+djZWVFjx49iq1PDQ0Njh8/TkBAAAkJCZiYmNCsWTNOnTpFpUqVit0vNzeXkSNH8uDBA3R0dPD09GT58oIfc1WqVOHAgQNMnDiR2rVrY2BgwMCBA+Ua9u+rcFTA5MmTadeuHZmZmVhYWODp6SltbE+YMIG+ffvi6OhIeno6d+7ckT7XXtSWLVtIS0tj/vz5zJ8/X7q+efPm0lf1uLu7s2PHDmbMmMG0adOwsbFh79691KxZ0PtXOFs4gIuLi0z+oaGhJf7c6OrqEhISwsiRI6lXrx5GRkbMnDlTpnd63bp1ZGVl8eWXX8rsW/j5KmlZ/vnnH/z9/UlJScHe3p4ff/yR3r17y6S/deuWzMiFwYMHc/ToUVxdXUlJSZHmd+zYMSZPnkzXrl15+fIlVapUoXXr1ujo6PDzzz9z4MABIiIiUFFRQUVFhW3bttGkSRM6duxI+/bti823JKrVbUZmShKXD2wjIzkBvao1aD58Dho6BT3naQnPkEhKfgNGIlEi6dEd7v5zhOz0VDR0DTC1r0Mtr69RVlWlXpM2pCQl8uf/fuJlwguqVLdhpP9SdP5/sqwXz57I/IA5fnAPOTnZbFwk+xlo32MAHXwGApCcEM/uTat4mfQCHX1DGrbwxLN7/xKXubS1aONJYkICW35aS0L8c6xs7Ji3fJ10sqynT+KQFLmpFf/8KcP7vnrc4tcdW/h1xxac67iydO0mAK5fu8KEkQOlaX5YWTCUt63XFyxZXPJhvZ7tvUh48YK1q1fy/Pkz7OwdWPvjTxh+oGGm/zbm6/tY29qzZNUP0uHNT+Iey9Rhrdp18P9+IRvWrmL9mhVUNbdg3pKV1LCW/ft2JOQg+fn5tPFUfKPypx9WE7z/d+n/e3zZuWD95p+p30DxTZXPpU5FvE8rZmnE+yc2iYrqKnSpZYKuhgqxCRksPXpH+tojQy1VmZFl2mrK9G9QFV0NFdKycrn7Ip3v/rr5UWaB/hStWbOGxYsXExcXR+3ataW/GYsTEBDAunXriI2NxcjIiC+//JL58+e/cTLV10nyi54hQRA+irt371K9enUiIiLkfvwLgvDfM/PQjbcnKmVNq8k/a/whtXUo2+dwY1+U/Y/JSjr/7cmdktPffZ6H96Wjqfr2RILwkQ37tWxfAxfoIz9/RmmrNPCXUsnn6cbub09URFBQEH369OGHH36gYcOGBAQE8OuvvxITE6OwY2PHjh0MGDCATZs24e7uzvXr1+nXrx89e/Zk2bJlJY4rhkMLgiAIgiAIgiCUYx9rOPSyZcsYPHgw/fv3x9HRkR9++AEtLS02bdqkMP2pU6do3LgxvXr1wtLSknbt2uHj4yM3B83biEawIAiCIAiCIAiC8N4yMzNJTk6WWQonLX1dVlYW58+fp02bV+8NV1JSok2bNsXOWePu7s758+eljd7bt29z4MCBN84Jo4hoBAvCJ8DS0pL8/HwxFFoQBEEQBEEoc6XVEzx//nzpKywLl6LzlhT1/PlzcnNzMTExkVlvYmJCXFycwn169erFnDlzaNKkCaqqqlhZWdGiRQumTZv2TscrGsGCIAiCIAiCIAjlWGk1gqdOnUpSUpLMMnXq1FIr59GjR5k3bx5r167lwoUL7N69mz///JO5c+e+Uz5idmhBEARBEARBEAThvamrq6OuXrJJA42MjFBWVubJkycy6588eYKpqanCfb799lt69+7NoEGDAKhVqxapqakMGTKE6dOnl/i1lqInWBAEQRAEQRAEoRz7GBNjqampUa9ePY4cOSJdl5eXx5EjR3Bzc1O4T1pamlxDV1lZGYB3eemR6AkWBEEQBEEQBEEoz959YudSMW7cOPr27YurqysNGjQgICCA1NRU+vcveOd8nz59qFKlivS5Ym9vb5YtW0adOnVo2LAhN2/e5Ntvv8Xb21vaGC4J0QgWBEEQhDIWEvGozGOu2vpur494XxnJyWUaL33f8DKNB5CRU+Yhy1RyetkfoHhPsPCuND5CayZo0foyjRfos7pM45WlHj168OzZM2bOnElcXBwuLi4EBwdLJ8uKjY2V6fmdMWMGEomEGTNm8PDhQ4yNjfH29ub7779/p7iS/HfpNxYEQRAE4b01WhBW5jFjLj8o03iiEfz5e/AivcxjVjXQLPOYwuftYzSCNeuMKtN46REfvhFcZfieUsnn4boupZLPhyZ6ggVBEARBEARBEMqxd32e93MnGsGCIAiCIAiCIAjlWHlrBIvZoQVBEARBEARBEIRyQ/QEC4IgCIIgCIIglGflqyNYNIIFQRAEQRAEQRDKMzEcWhDKCUtLSwICAj5K7KNHjyKRSEhMTPwg+c+aNQsXF5cPkndRH/o4BOHfCAwMRE9Pr9TTCoIgCILw3yAawcJ/3qf4I9fd3Z3Hjx+jq6v7sYvySRg6dChWVlZoampibGxMp06duHbt2scuVqlo0aIFY8eOfWu6o0eP0qlTJypXroy2tjYuLi5s375dLt2vv/6Kvb09Ghoa1KpViwMHDki3ZWdnM3nyZGrVqoW2tjZmZmb06dOHR48Uv5M2MzMTFxcXJBIJkZGRMtuioqJo2rQpGhoamJubs2jRIpntGzZsoGnTpujr66Ovr0+bNm34559X76EtaVkuXLhA27Zt0dPTw9DQkCFDhpCSkvLW+nqTHj16cP369VJP+yF0q2vGnuENCZvQlI196uBYueIb01dQV2ZCW2v2j2rEsQlN+WVIfdxqGEi37xnekNNTmsstE9paAzCojS0Xl3fi8aaeHJ7lQd0ahm+MN8zDjn8We/NoUw8ur+jM9751UVd99dNhQGsbTszz4t6G7tzb0J1D/u1o42wmk8dQLyeubfAl4bfBHFvcFVebSm+MOeoLZy6u9eHFr4O5sbE3iwa6o66q/KoONFVZPKgxMT99zYtfBxO6sAv1rI3fmOenYueO7bRv24r6dWrh2/MrLkVFfRYx9+/eSf+v2tO5dQO+GfI1MVcvFZv23p2bfD9jPP2/ak+Hpi7s/WXbG/P+ZdsmOjR1Yf3KRW9MV5zPtU5FvI8fs6w0rmvFbwFDuR3yPekRq/Fu4fyxi6SQRCIpleVzIRrBgvAOsrOzSyUfNTU1TE1NP6sviw+pXr16bN68mejoaA4dOkR+fj7t2rUjNzf3YxetzJw6dQpnZ2d27dpFVFQU/fv3p0+fPuzfv18mjY+PDwMHDiQiIoLOnTvTuXNnLl++DEBaWhoXLlzg22+/5cKFC+zevZuYmBi++OILhTEnTZqEmZmZ3Prk5GTatWuHhYUF58+fZ/HixcyaNYv169dL0xw9ehQfHx9CQ0MJDw/H3Nycdu3a8fDhwxKX5dGjR7Rp0wZra2vOnDlDcHAwV65coV+/fu9Vl5qamlSq9OaG1r9JW9ra2BszppUVP524S9/N57nxNIWAHrXQ11JVmF5FScLKns5U1tVg2p6r9NjwD/MPXudZSqY0Tf/AC3itOiVdRv/vIgB/xzyjS0MLvvOty8I9l2gx4wCXYxPYNbklRjrqCuN96WaJf486LNp9iYaT9jN6w2m6NLLg2+4u0jSPXqQxOyiSljMO0urbgxy/+oTt45phX6XgBt+XTaxYOLAx3+88h9s3vxF1N559sztirKv4XbA9mtkwt09D5u08h8vInQxbFcqXTayZ07uhNM26US1o5VKVAcuP4OoXxF+R9/lzrvc71f3HEHzwAEsWzWfoiJHs/HUPdnb2DB86kPj4+E865rEjh9iweim9+g1l5U//o7q1Ld+OH0FiwguF6TMzMjCtXIV+Q8egb2D0xryvR18meN9vVLeyfafjKvS51qmI9/FjliVtTXUuXX/I2PlBH7sobyQawYLwiQkODqZJkybSnqKOHTty69YtAO7evYtEImH37t20bNkSLS0tateuTXh4OFDwQ71///4kJSVJP5yzZs2S5p2WlsaAAQOoWLEi1apVk/mRX5h3UFAQzZs3R0NDg+3bt5OXl8ecOXOoWrUq6urquLi4EBwcLLffzp07cXd3R0NDg5o1axIWFiZNo2gY8cmTJ2nRogVaWlro6+vj4eFBQkJCsfXy4MEDfHx8MDAwQFtbG1dXV86cOaMw7dmzZ2nbti1GRkbo6urSvHlzLly4IFfmor2BiYmJSCQSjh49Kl134MABbG1t0dTUpGXLlty9e1cu1okTJ2jatCmampqYm5vj5+dHampqsccBMGTIEJo1a4alpSV169blu+++4/79+wrzL3Tv3j28vb3R19dHW1sbJycnmV7RsLAwGjRogLq6OpUrV2bKlCnk5ORIt7do0QI/Pz8mTZqEgYEBpqamMtcGFPxB+Omnn+jSpQtaWlrY2Niwb98+mTSXL1+mffv2VKhQARMTE3r37s3z588B6NevH2FhYaxYsUJ6/RV3TNOmTWPu3Lm4u7tjZWXFmDFj8PT0ZPfu3dI0K1aswNPTk4kTJ+Lg4MDcuXOpW7cuq1evBkBXV5fDhw/TvXt37OzsaNSoEatXr+b8+fPExsbKxDt48CAhISEsWbJErizbt28nKyuLTZs24eTkRM+ePfHz82PZsmUyaUaMGIGLiwv29vb89NNP5OXlceTIkRKXZf/+/aiqqrJmzRrs7OyoX78+P/zwA7t27eLmzZsK6ykkJAQNDQ25IfhjxoyhVatWgPzoj4sXL9KyZUsqVqyIjo4O9erV49y5cwrTAqxbtw4rKyvU1NSws7Nj69atMttLcl2UhE+Dqvx+8TF/XnrC3fg0FgbfICM7j47OpgrTezuboqOhyqTdV4h6mMzjpEwi7idx8+mrz1diejYvUl8tja0NuZ+QzoXYJEa0t+fn0JvsOHabmEfJjNv8D2mZuXzd3EphvAY2Rpy58Yzfwu9y/3kqoZfj2BV+j3pFeo+DIx5y+OIjbj95ya24l3z360VSM3JwtS5o/Ph1qs3mkKtsPRLDtfsJjF4bRnpmNn3b2CuM2cjBhPDoOIKO3SD26UuORD7gl+M3cLUtuFGhoaZMZ/caTA8M5+SVx9x+nMz3/zvHrcfJ71z/ZW3rls10/bI7nbt0w8ramhn+s9HQ0GDv7l2fdMw9QVvx9O5K2w6dqVbdilETZqChoUHIn3sVprd1qMnAkeNo3sYTVTXFN3QA0tPSWDxnGqMnzaRCxTePgCjO51qnIt7Hj1mWQk5eZfba/ewL/e/0bv8XiEaw8MlLTU1l3LhxnDt3jiNHjqCkpESXLl3Iy8uTppk+fToTJkwgMjISW1tbfHx8yMnJwd3dnYCAAHR0dHj8+DGPHz9mwoQJ0v2WLl2Kq6srERERjBgxguHDhxMTEyMTf8qUKYwZM4bo6Gg8PDxYsWIFS5cuZcmSJURFReHh4cEXX3zBjRs3ZPabOHEi48ePJyIiAjc3N7y9vYu9qxkZGUnr1q1xdHQkPDycEydO4O3tLe0JDQwMlLm7lpKSQvPmzXn48CH79u3j4sWLTJo0SaZOinr58iV9+/blxIkTnD59GhsbG7y8vHj58mWJz8P9+/fp2rUr3t7eREZGMmjQIKZMmSKT5tatW3h6etKtWzeioqIICgrixIkTjBo1qsRxUlNT2bx5M9WrV8fc3LzYdCNHjiQzM5Njx45x6dIlFi5cSIUKFQB4+PAhXl5e1K9fn4sXL7Ju3To2btzId999J5PHli1b0NbW5syZMyxatIg5c+Zw+PBhmTSzZ8+me/fuREVF4eXlha+vLy9eFPSAJCYm0qpVK+rUqcO5c+cIDg7myZMndO/eHShotLq5uTF48GDp9femY3pdUlISBgavhrqGh4fTpk0bmTQeHh7Smz7F5SGRSGQaek+ePGHw4MFs3boVLS0tuX3Cw8Np1qwZampqMnFiYmKKvTGTlpZGdna2THnfVpbMzEzU1NRQUnr1p0hTs6CH8MSJE9J1lpaW0hsUrVu3Rk9Pj127Xv04ys3NJSgoCF9fX4VxfX19qVq1KmfPnuX8+fNMmTIFVVXFP8737NnDmDFjGD9+PJcvX2bo0KH079+f0NBQmXRvui5KQkVJgp1pRc7efVWf+cDZuwnUqqKjcJ+mNoZcfpjMxHbWHBjtxvaBrvR1q4ZSMTfeVZQkeDqZsD8qDhUlCS7VDTh6Je5VvHwIuxJHfWvFvXX/3HiOi6WBdMi0hXEF2tY24/BFxcPrlSQSujayQEtdhbM3nqGqokQda2P+jnwgE/Pviw9pYG+iMI/T0U+oY2UsHTJtaVIRj3oWBJ8vuHGioqyEirISGVmyo0QysnLk8vqUZGdlEX31Co3c3KXrlJSUaNTInaiLEZ9szOzsbG5ej8al3queeCUlJVxcG3Ltyvv9oF+3fB713ZpSx7XRv9r/c61TEe/jxxQUK289wWJ2aOGT161bN5n/b9q0CWNjY65evSpt9EyYMIEOHToABT9OnZycuHnzJvb29ujq6iKRSDA1le9d8fLyYsSIEQBMnjyZ5cuXExoaip2dnTTN2LFj6dq1q/T/S5YsYfLkyfTs2ROAhQsXEhoaSkBAAGvWrJGmGzVqlLTs69atIzg4mI0bNzJp0iS5cixatAhXV1fWrl0rXefk5CT9t66urkyZduzYwbNnzzh79qy00WFtbV1sHRb2kBVav349enp6hIWF0bFjx2L3K6qwd2zp0qUA2NnZSRufhebPn4+vr6/0GVgbGxtWrlxJ8+bNWbduHRoaGsXmv3btWiZNmkRqaip2dnYcPnxYphH2utjYWLp160atWrUAqFGjhkxe5ubmrF69GolEgr29PY8ePWLy5MnMnDlT2uhydnbG399fWtbVq1dz5MgR2rZtK82rX79++Pj4ADBv3jxWrlzJP//8g6enJ6tXr6ZOnTrMmzdPmn7Tpk2Ym5tz/fp1bG1tUVNTQ0tLS+H19ya//PILZ8+e5ccff5Sui4uLw8REtvFgYmJCXFzc67sDkJGRweTJk/Hx8UFHp6BhlZ+fT79+/Rg2bBiurq4Ke6bj4uKoXr26XJzCbfr6+nL7TJ48GTMzM7lG+pvK0qpVK8aNG8fixYsZM2YMqamp0hsrjx8/lu5rZWWFkVFBQ01ZWZmePXuyY8cOBg4cCMCRI0dITEyU+64oFBsby8SJE7G3L+h9tLGxUZgOCj7f/fr1k34vjBs3jtOnT7NkyRJatmwpTfem66Ik9LRUUVGS8CJV9hGLhNRsLA3lb0wAmOlpUs9Cg0NXnvDNL5cw19dkoocNKkoSNp68J5e+ua0RFTRU+PNSXEE8ZSWeJWXIpHmWlIFNZcWN7t/C72JQUZ2DM9siQYKqihKb/rrOsn1XZNI5VtXj0Kx2aKgqk5qRQ++AY8Q8SsZIRwMVZSWeJqbLpH+amIZdFT2FMYOO3cBQR4MjCzojkYCqijLrD15h8a8FI1dS0rM5HR3H1B71iHmQwJPEdLo3s6ahneJG9aciITGB3NxcDA1ln8E2NDTkzp3bn2zM5KQE8nJz0TOQzUNP35D79+7+67KF/RXMzevXCFgvP+9BSX2udSriffyYQjE+n/ZrqRA9wcIn78aNG/j4+FCjRg10dHSwtLQEkBne6ez8apKBypUrA/D06dO35l10v8KG8uv7ubq6Sv+dnJzMo0ePaNy4sUyaxo0bEx0dLbPOzc1N+m8VFRVcXV3l0hQq7AkuTpcuXWQmioqMjKROnTpv7HUrqrDnz8bGBl1dXXR0dEhJSZEbIvsm0dHRNGzYUGZd0WOEgmGngYGBVKhQQbp4eHiQl5fHnTt3mDdvnsy2ovF9fX2JiIggLCwMW1tbunfvTkZGwQ92Jycn6T7t27cHwM/Pj++++47GjRvj7+9PVJFJNKKjo3Fzc5O5I9m4cWNSUlJ48OBVr1TR8w8F187r579oGm1tbXR0dKRpLl68SGhoqMwxFTa0CofsK6LoeIoKDQ2lf//+bNiwQeZmyLvIzs6me/fu5Ofns27dOun6VatW8fLlS6ZOnfqv8lVkwYIF7Ny5kz179ii80VFcWZycnNiyZQtLly6V3iioXr06JiYmMr3DR44ckRlN4Ovry9GjR6WTbG3fvp0OHToUOwHeuHHjGDRoEG3atGHBggVvPDfR0dEl+ny/6bp4XWZmJsnJyTJLXs67zy+gJIGE1CwWBF8n5kkKf117RuCpWLrUqawwvbezKadvv+B5StY7xwJo7FCJcV84MSHwLC1mHOTrgDDauVRhQueaMuluPE6m2fQDtPE/xKYjN1g71A07M8UN67dpWtOMiV/VZcwPx3H75jd6zAumvWs1pvSoJ00zYPkRJBIJtwP7krRrCCM71uKX44qHzwufnmdP4li/chETv52Hmrri59EFQRA+NNETLHzyvL29sbCwYMOGDZiZmZGXl0fNmjXJynr1w67o0MbChk9xQ4OLen1IpEQikdtPW1v7fYpfIoVDQD9U+r59+xIfH8+KFSuwsLBAXV0dNzc3aR0WNjjy8/Ol+/ybScBSUlIYOnQofn5+ctuqVavGsGHDpEOFAZlJmXR1ddHV1cXGxoZGjRqhr6/Pnj178PHx4cCBA9LyFB77oEGD8PDw4M8//yQkJIT58+ezdOlSRo8eXeLyluT8vylNSkoK3t7eMr3hhQpvxiii6HgKhYWF4e3tzfLly+nTp4/MNlNTU548eSKz7smTJ3K9zIWNznv37vH3339Le14B/v77b8LDw1F/7cenq6srvr6+bNmypdg4hWUoasmSJSxYsIC//vpL7qbC28oC0KtXL3r16sWTJ0/Q1tZGIpGwbNkymZ7919WvXx8rKyt27tzJ8OHD2bNnD4GBgcWmnzVrFr169eLPP//k4MGD+Pv7s3PnTrp06VLsPm9Tkmun0Pz585k9e7bMOkuPAeT4NcVAWzYffW1V4lMVN1qfp2SRm5dP3quPKXfj0zCqoI6KkoScIhtMddSpb6nPlD0FvbaJadnk5OZhrCt7k8JYV4OnSbI9tYWmf1mbX07eYevRgpsGVx8koq2uwvIBDVn6+2UKvy6yc/O486RgRu+Ld19Qp4YBwzzt8Vv9Nzm5eVTSk73GK+lpEZeYpjCmv28D/hd6ncDDBTcdrtx7gZaGCmtGNmfhL+fJz4c7ccm0m/Y7Wuoq6GipEZeQxtaJbRXm96nQ19NHWVlZ7pGY+Ph46SiHTzGmjq4+SsrKJL6QzSMxIR59w39X7psxV0lMeIHfIB/purzcXC5fvMAfu4M4F3EJZWXlN+RQ4HOtUxHv48cUFPuchjKXBtETLHzS4uPjiYmJYcaMGbRu3RoHB4c3ThaliJqaWqnNMqyjo4OZmRknT56UWX/y5EkcHR1l1p0+fVr675ycHM6fP4+Dg4PCfJ2dnaUTCpWEs7MzkZGRJX4G8eTJk/j5+eHl5YWTkxPq6urSyZsAjI0LXi9SdAjq66/McXBwkHkFDsgeI0DdunW5evUq1tbWcouamhoGBgYy61RUFN+Hy8/PJz8/n8zMgllvLSwspPtUqVJFms7c3Jxhw4axe/duxo8fz4YNG6RlDQ8Pl2nUnzx5kooVK1K1atUS1VlJ1K1blytXrmBpaSl3vIU3TxRdf8Udz9GjR+nQoQMLFy5kyJAhcvHc3NzkrpPDhw/L9MgXNjpv3LjBX3/9JTfEbOXKlVy8eJHIyEgiIyOlk4kFBQXx/fffS+McO3ZM5kbI4cOHsbOzkxkKvWjRIubOnUtwcLDMiImSlqUoExMTKlSoQFBQEBoaGjJD0hXx9fVl+/bt/PHHHygpKUkfhyiOra0t33zzDSEhIXTt2pXNmzcrTOfg4FCiz/e7mDp1KklJSTKLcZOexMS9pL7lq/qUAPUt9Ln0UPEkT1EPkqmqrykzYs3cQJNnLzNlGsAAHZ1NSUjL4tTNgh+WOXn5RN55QXOnVzcxJBJo5mTK2ZvPUURTTZm81/LN/f//S94wbk5JIkFNRYnsnDwibj6jZe1XnzmJBFo6V+Gfa08U7quprkJevmzMwjK8/gMtLTOHuIQ09LTVaFOn5M/afwyqamo4ODpx5vSr5/fz8vI4cyYc59p1PtmYqqqqWNs6EHn+1Xd/Xl4ekef/wd7p373mpbZrQ9Zs+Y1Vm4Kki429Iy3aerFqU1CJGsDw+dapiPfxYwqKlbdngkUjWPik6evrY2hoyPr167l58yZ///0348aNe6c8LC0tSUlJ4ciRIzx//py0NMU9ECU1ceJEFi5cSFBQEDExMUyZMoXIyEjGjBkjk27NmjXs2bOHa9euMXLkSBISEhgwYIDCPKdOncrZs2cZMWIEUVFRXLt2jXXr1kkbqnv27JEOswXw8fHB1NSUzp07c/LkSW7fvs2uXbuKnSDJxsaGrVu3Eh0dzZkzZ/D19ZXpgdTU1KRRo0YsWLCA6OhowsLCmDFjhkwew4YN48aNG0ycOJGYmBh27Ngh1/s2efJkTp06xahRo4iMjOTGjRv8/vvvb5wY6/bt28yfP186a/CpU6f46quv0NTUxMvLq9j9xo4dy6FDh7hz5w4XLlwgNDRUepNhxIgR3L9/n9GjR3Pt2jV+//13/P39GTdunMww2/c1cuRIXrx4gY+PD2fPnuXWrVscOnSI/v37Sxu+lpaWnDlzhrt37/L8+fNiewtDQ0Pp0KEDfn5+dOvWjbi4OOLi4mRudIwZM4bg4GCWLl3KtWvXmDVrFufOnZPWb3Z2Nl9++SXnzp1j+/bt5ObmSvMp7PWvVq0aNWvWlC62tgWvJrGyspLeIOjVqxdqamoMHDiQK1euEBQUxIoVK2Q+ewsXLuTbb79l06ZNWFpaSuMUvuO3JGUBWL16NRcuXOD69eusWbOGUaNGMX/+fJmhza1bt5bOgF3I19eXCxcu8P333/Pll1/K9WwXSk9PZ9SoURw9epR79+5x8uRJzp49W+wNqYkTJxIYGMi6deu4ceMGy5YtY/fu3TIT6r0rdXV1dHR0ZBYlFTX+988DvqhdGa+aJlgaajHJwwYNNSX+jCp4xntmRzuGN3/1bPbuiEfoaKgwrq015vqauFsZ0M+tGrsuyE5UJQE61DLlwKUn5BZpT649eI0+Lazp2bQ6tmY6LOvfAG11ZbaHFTx3t26oGzOLvP4oOOIh/dvY0rWRBdWMtWlR05RpX9YmOOKhtKE6s7sL7naVMDfSxrGqHjO7u9DEwYRfT90FYOXvF+nfzgHfVnbYVdVj5fBmaGmo8vORgsc7fhrbijl9Xj1mceDsXQa3d+KrptZYmFSklUtVZvo24MA/96SN4TZ1zGlb11y6Pfj7Tlx/mPivz09Z6d23P7t/+4V9e/dw+9Ytvpszi/T0dDp36frWfT9mzC49enNo/27+OriP2Lu3WbP0ezLS02nr1QmApd/NIPCHldL02dnZ3LpxjVs3rpGTnUP8s6fcunGNRw8KHn/R0tLGsoa1zKKhoYmOri6WNYqf2+JDHd+7KuuY//V4HytmWdLWVMPZtgrOtgU3vS2rGOJsWwVzU/n5NT4miaR0ls+FGA4tfNKUlJTYuXMnfn5+1KxZEzs7O1auXEmLFi1KnIe7uzvDhg2jR48exMfH4+/vL/cqnHfh5+dHUlIS48eP5+nTpzg6OrJv3z65yXYWLFjAggULiIyMxNramn379hU7tMfW1paQkBCmTZtGgwYN0NTUpGHDhtKJd5KSkmRmrVZTUyMkJITx48fj5eVFTk4Ojo6OMhNzFbVx40aGDBlC3bp1MTc3Z968eXI/6jdt2sTAgQOpV68ednZ2LFq0iHbt2km3V6tWjV27dvHNN9+watUqGjRowLx582Qa9s7OzoSFhTF9+nSaNm1Kfn4+VlZW9OjRo9j61NDQ4Pjx4wQEBJCQkICJiQnNmjXj1KlTb3x/a25uLiNHjuTBgwfo6Ojg6enJ8uXLAahSpQoHDhxg4sSJ1K5dGwMDAwYOHCjXsH9fhaMCJk+eTLt27cjMzMTCwgJPT09pY3vChAn07dsXR0dH0tPTuXPnjvS59qK2bNlCWloa8+fPZ/78+dL1zZs3l76myt3dnR07djBjxgymTZuGjY0Ne/fupWbNgmc0C2cLB3BxcZHJPzQ0tMSfG11dXUJCQhg5ciT16tXDyMiImTNnyvROr1u3jqysLL788kuZfQs/XyUtyz///IO/vz8pKSnY29vz448/0rt3b5n0t27dkhm5AAUTwTVo0IB//vmHgICAYo+lcJhdnz59ePLkCUZGRnTt2lVueHKhzp07s2LFCpYsWcKYMWOoXr06mzdvfqfvnJL669oz9LRUGdzUEkNtNW48TeGboEu8SCvogTfV0aBop+jTl5mMCbrE2NZWbBvoyrOXmQSde8jW07LP9te31KeyrgZ/RMlOmLbnzD2MdNSZ1q02lXQ1uHQvgS8XhfIsueDZ+6pG2jK9sEv2Fgx5nv5VbSrraxKfnElwxEPm/hopTWOko866YW6Y6GmSnJbNlfsJdFv0N0cvF8T+7cQtjHQ1mdmrPib6WkTdfk6nWfulk2WZG1eQibkgqGDIs//XDTAz0OZ5cjp//nOPWdtevf5NV0uNOX0aUsWoAi9eZvB7+G38t/7Dk50D3+NsfHie7b1IePGCtatX8vz5M+zsHVj7408YfsAhn6URs1lrD5ISE9i2cR0JL55Tw9qOOUvWov//k2U9e/JYpvfnxfOn+A3oKf3/7p0/s3vnz9RyqceCVRtL7+D4fOtUxPv4MctSXUcLQn561VGyaELBJI5b951miP+2j1Wsck+Sn//auCNBEN7L3bt3qV69OhEREXI//gVBEAAaLQh7e6JSFnP5wdsTlaKM5LJ9d2/6vuFlGg8g49N+M9N7e/BC8fPiH1JVg3eb80IQND5Cl55mnZK/+rE0pEesfnui92QzMbhU8rmxuGRvSfjYRE+wIAiCIAiCIAhCOfY5DWUuDeKZYEEQBEEQBEEQBKHcED3BglDKLC0tEU8ZCIIgCIIgCJ+Lz2lm59IgGsGCIAiCIAiCIAjlWDlrA4vh0IIgCIIgCIIgCEL5IXqCBUEQBEEQBEEQyjElpfLVFSwawYIgCIIgCIIgCOWYGA4tCIIgCIIgCIIgCP9RoidYEARBEMrYxaBfyzxmB78BZRrvz5WbyjSeZp1RZRoPID1idZnGy8gp03DU8phYtgGBv3/9rkzjVdQs25/C1iYVyjTex+Cx6mSZxgv7pnGZxvuvErNDC4IgCIIgCIIgCOVGOWsDi0awIAiCIAiCIAhCeVbeeoLFM8GCIAiCIAiCIAhCuSF6ggVBEARBEARBEMqx8tYTLBrBgiAIgiAIgiAI5Vg5awOL4dCCIHx4R48eRSKRkJiYWOaxAwMD0dPTe+98JBIJe/fufe98PvX4/fr1o3Pnzh88jiAIgiAIwsciGsGCIHxw7u7uPH78GF1d3bem/ZgN5jd5/Pgx7du3L3F6S0tLAgICPlyBFPDz86NevXqoq6vj4uIit/3o0aN06tSJypUro62tjYuLC9u3b3/vuNnZ2UyePJlatWqhra2NmZkZffr04dGjRwrTZ2Zm4uLigkQiITIyUmZbVFQUTZs2RUNDA3NzcxYtWiQXa86cOVhZWaGhoUHt2rUJDg5+Y/nu3r2rMNb7+lD5KtK4rhW/BQzldsj3pEesxruF87/Kx9PeiLVfOrGjtwvzO9phbaRVbNoW1gb81r+uzLKjt4tMmte3Fy5f1Kz0TuUqreP71GN+DDt3bKd921bUr1ML355fcSkq6oPFKq06/Wv/r4zv35lBnZsy55sB3I65Umzao8F7mTdpCCO6t2FE9zYsmjZKLv2GZXPo16GhzLLk2zEyaQ7u/YVhPh3p6eHGlBF9uBF9udiYsXdusch/IsN8OtKtVT32/7ZDLk1ubi7/27SW4b288fF0Z4TvF/y6dQP5+fnvWBtlew5LM17n2qbsHFCPkNFurOvpjP1bXg9VQV2ZsS1rsHtIfQ6PdmNbv7o0tNRXmLZX/SqEfdOYUc2r/6uylaXP5btGIpGUyvK5EI1gQRA+ODU1NUxNTUv1yzErK6vU8ioJU1NT1NXVSzXP3Nxc8vLySjXPAQMG0KNHD4XbTp06hbOzM7t27SIqKor+/fvTp08f9u/f/14x09LSuHDhAt9++y0XLlxg9+7dxMTE8MUXXyhMP2nSJMzMzOTWJycn065dOywsLDh//jyLFy9m1qxZrF+/XppmxowZ/Pjjj6xatYqrV68ybNgwunTpQkRExHsdw6dOW1OdS9cfMnZ+0L/Ow726Pn0bVOXXyMdM2neNuy/SmdHOGh2N4p+MSs3KZdDOKOky/FfZhkHRbYN2RrHm+F3y8vM5fTfxncpWGsf3rj5GzLIWfPAASxbNZ+iIkez8dQ92dvYMHzqQ+Pj4DxKvNOr0zLHD7Nywgs69BjJ75RbMq1uz5NsxJCe+UJj+2qULNGzWjsnz1zJj6U8YGFdi8bd+JDx/KpOuVj03ArYekC7DJ82VbjsZGkLgumV07zOExT9ux8LKlrmTR5GUoDhmVmYGJpWr8PXg0egZGCpMs3fnFg7t+41BfpNYEfgbvYf4sXfnz+zYvvWd6qOsz2FpxWtpa8TIZtXZcvo+g7dHcut5Kku6OqGnqaowvYqShKVdnTDVUWfm/mv03nKBxYdv8jwlUy6tvUkFvqhlys1nqf/qGMva5/JdI5GUzvK5EI1gQRDeWYsWLRg9ejRjx45FX18fExMTNmzYQGpqKv3796dixYpYW1tz8OBBQL539969e3h7e6Ovr4+2tjZOTk4cOHCAu3fv0rJlSwD09fWRSCT069dPGnPUqFGMHTsWIyMjPDw8AFi2bJm0B9Lc3JwRI0aQkpLyTsdjaWnJ3Llz8fHxQVtbmypVqrBmzRqZNEWHI//8889UqFCBGzduSLePGDECe3t70tLSaNGiBffu3eObb76RuTNaODR73759ODo6oq6uTmxsLGfPnqVt27YYGRmhq6tL8+bNuXDhwjsdA8DKlSsZOXIkNWrUULh92rRpzJ07F3d3d6ysrBgzZgyenp7s3r1bLu3s2bMxNjZGR0eHYcOGvfGmg66uLocPH6Z79+7Y2dnRqFEjVq9ezfnz54mNjZVJe/DgQUJCQliyZIlcPtu3bycrK4tNmzbh5OREz5498fPzY9myZdI0W7duZdq0aXh5eVGjRg2GDx+Ol5cXS5cuLbZ81asX9BTUqVMHiURCixYtpNt++uknHBwc0NDQwN7enrVr10q3DRgwAGdnZzIzC36EZWVlUadOHfr06fPWfEtbyMmrzF67n32h/74HyNupEn9df07ozRc8SMpg/alYMnPyaGWj+Ec8APn5JKbnSJekjByZzUW3JabnUL+aHlcev+RpyrvdpCqN43tXHyNmWdu6ZTNdv+xO5y7dsLK2Zob/bDQ0NNi7e9cHiVcadXpoz/9o7tmJpm29qVKtBn1HTUFNQ4NjIX8oTD9s4hxad/wSCytbzMwtGeA3nfy8PK5ePCeTTkVVFT0DQ+miXVFHuu2PX7fRxqsLrdp/gfn/sXfncTVn/wPHX7d93yUS0SoqZKvGzkTGjHWMaULDWJMMBl9rBonBWIYxmCnGNtYx9jVG9jZrIRJNqIQ27b8/+rlc3Sha0Hl6fB4P93PP57zP+dxPdc/nLB/zegwd8z9UVdU4su9vuTEtbRswYJgvn7R3Q1lZRW6a6CuRNHNti1PLVhib1MS5TUccm7bk8qXSnZuK/gzLKt6XTWqy+/ID9l19yJ1HmSw4HMOz3Dzcixkl4t6wOtpqSkz+J4rL/6Vy/2kWkfFPiUnKkEmnrqzAlC7WzD98k9RXfh+9r6rC75oPkWgEC4LwVoKCgjAyMuLcuXOMGjWK4cOH06dPH1xcXAgLC+PTTz/F09OTjIyMIseOHDmSrKwsTpw4waVLlwgICEBLSwszMzO2bSv8QxsdHU1CQgKLFy+WiamiokJISAi//vorAAoKCixZsoQrV64QFBTE0aNH+eGHH4ot9/MhrMHBwTL758+fj6OjI+Hh4UycOJHRo0dz6NAhuXn0798fd3d3PDw8yM3NZc+ePaxevZr169ejoaHB9u3bqVWrFjNnziQhIYGEhATpsRkZGQQEBLB69WquXLmCsbExqampDBgwgJMnT3LmzBmsrKxwd3cnNTW1xJ/H23ry5AkGBgYy+44cOcK1a9cIDg5m48aNbN++HT8/v1LnK5FIZOZjP3jwgO+++45169ahoVF0GO7p06dp3bo1KiovvlS6ubkRHR1NSkoKUDiUWk1NTeY4dXV1Tp48KX0dGBgoM+rg3LlzABw+fJiEhARpo3/9+vVMmzaN2bNnc+3aNebMmcPUqVMJCgoCCm8qpKenM3HiRAAmT57M48ePWbZs2WvzfR8pKylSz1CDi/+9uKYKgEsJqdgYaxZ7nJqyIiv6NODXLxsyoUM9aumpFZtWV02JJma6HLlRPj1UQunkZGdz7eoVWjq7SPcpKCjQsqULFyPf05ETBfnE3ozCrlFz6S4FBQUaNGpGTNSlEmWRlfWMvLw8mUYuFPYYj/q6MxOH9CHolwDSnj4BCqdYxFyPwsFJNqaDU3OuXy1ZTHlsGjhyKewc/929A0BszHWiLkfwSavWJc6joj/DsoqnpCDBuroWoXGPpfsKgNC4JzSooS33GNd6+lxJSGVM+3rsGNKMPzwb8U2zWii80rPo296C07dTCI17UpqqCSVQ1YZDi9WhBUF4K46OjkyZMgWASZMmMXfuXIyMjPjuu+8AmDZtGitWrOCinLlEcXFx9OrVC3t7ewCZnsvnDTJjY+MiC1pZWVkVmSPq6+sr/b+5uTmzZs1i2LBhMj16L1NWVsbGxqZII8zV1VXa2LG2tiYkJIRFixbRqVMnufmsXLkSBwcHfHx82L59OzNmzMDJyUlaB0VFRbS1tTExMZE5Licnh+XLl+Po6Cjd1759e5k0v/32G3p6ehw/fpzPPvtMbvyy8Ndff3H+/HlWrlwps19FRYXff/8dDQ0NGjRowMyZMxk/fjw//vgjCgpvvnf67NkzJkyYQL9+/dDRKfwiWlBQwMCBAxk2bBhNmzYlNja2yHH379+X9q4+V716del7+vr6uLm5sXDhQlq3bo2FhQVHjhxh+/bt5OXlSY/R1dXFxsZG+rpatWoAGBoaynwe06dPZ8GCBfTs2RMo7Nm9evUqK1euZMCAAWhpafHnn3/Spk0btLW1+fnnnzl27Ji0TsXl+z4y0tdCUUHCk8yiPbmmuvIbtv89yWL5yTvcSclEQ1mRzxtWZ3ZXG8bsuMqjjJwi6dtaGpKZk8fZO4/LowpCKaU8TiEvLw9DQ9mefkNDQ27fvlVJpXo9xYJs8vPz0NWTvTGno2dAwv83Jt9kyx+/oGdghF2jZtJ99k4taerSFiOTmjxMiGdb0HIWTPdl6k+rSU1/Sn5+Hnr6sudJV9+Q+LjYt65Lj34DyUhPw2dgLxQUFMjPz+frQSPo+pn8aSLyVPRnWFbxdNWVUVKQkPLK74mUjGxq68tfG6SGrhqNzdQ4HJXIhJ1XMdVTZ0z7eigqSgg6cxeA9tZGWBtrMnRDZClrJpTEB9R+LROiJ1gQhLfi4PBiYQdFRUUMDQ2ljVp40Xh5+PBhkWN9fHyYNWsWrq6uTJ8+XW5DWZ7njcyXHT58mA4dOmBqaoq2tjaenp4kJyfL7YEGMDU1JSoqiubNm8vsd3Z2LvL62rVrxZZFX1+fNWvWsGLFCiwsLKQN6DdRUVGROXfwoofUysoKXV1ddHR0SEtLKzKU+LkuXbqgpaWFlpYWDRo0KFHcVx07dgwvLy9WrVpVJA9HR0eZmwTOzs6kpaVx9+5d1q9fL42tpaXFv//+K3NsTk4OX375JQUFBaxYsUK6f+nSpaSmpjJp0qS3Ku9zixcvxsrKCltbW1RUVPD29sbLy0umcd6jRw+ioqJem096ejoxMTEMGjRIpj6zZs0iJiZGpu7jxo3jxx9/ZOzYsXzyySelLnNWVhZPnz6V2Qry8958YCW7npjO8ZhHxD7K5OqDNOYfjeHpsxw+tTGSm769lSH/xjwiJ6/0C/8IQlnY/VcQZ08cwmdKACoqL9ZwaNnmUxq3bI2ZuSVOzm3wnb6Q29evEnWp9NNOSupU8CH+PbIf38mzmb9yPd4T/Pj7rz/ZtXNHucX8kClIJDzOyOGnwze5/jCdY9eT+PPcPb5wKLzBWE1LhVFt6/Ljvutki98x5UL0BAuCIJSAsrLs4hYSiURm3/NfhPIWfho8eDBubm7s2bOHgwcP4u/vz4IFCxg1atRrY2pqyg7bjI2N5bPPPmP48OHMnj0bAwMDTp48yaBBg8jOzpY75LYsnThxAkVFRRISEkhPT0dbW/4wr5epq6sX+SMxYMAAkpOTWbx4MXXq1EFVVRVnZ+di5+GuXr2azMxMoOjnUBLHjx+nW7duLFq0SDq/taQ+//xzWrRoIX1tamoq/f/zBvCdO3c4evSotMcU4OjRo5w+fbrI4mJNmzbFw8ODoKAgTExMePDggcz7z18/72mtVq0aO3fu5NmzZyQnJ1OzZk0mTpxY7Dzo4jyfN75q1SqZ+kDhTZ3n8vPzCQkJQVFRkZs3b5YqxnP+/v5FhpMrVm+Gco3mxRxR9pJS0sjLL0BXXfbPvp66Eo8zi/bqypNXALHJmZjoFF0grn51TUz11FgYfLtMyiu8O309fRQVFYssaJScnIyRkfwbGZUtT6KCgoIiT15ZBOvp40fo6hsUc1Shfdv+ZM/Wtfwwexlmda1em9a4hinaOno8SLhLk6ZNUVBQ5HGK7Hl6kpKMnsHbn6e1KxfTo99APmlfuH5FnXpWJD1IYM3qlXzevUeJ8qjoz7Cs4j3JzCE3vwB9Ddm/T/oaKjzKkP93LTk9m9z8AvJfat/eeZSBoaYKSgoSbKprYaCpwiqPRtL3lRQkONbSKZqZIJSA6AkWBKFSmJmZMWzYMLZv387YsWNZtWoVgHQ+6MvDW4sTGhpKfn4+CxYsoGXLllhbWxf7WJ43OXPmTJHX9evXLzb9qVOnCAgI4J9//kFLSwtvb2+Z91VUVEpUB4CQkBB8fHxwd3enQYMGqKqqkpSUVGx6U1NTLC0tsbS0pE6dOiWK8VxwcDBdu3YlICCAIUOGyE0TGRkpbWRD4bl4Pmf7+aJnzzd1dXXgRQP4xo0bHD58uMhwuiVLlhAZGUlERAQRERHs3bsXgM2bNzN79mygsNf1xIkT5OS8aJQdOnQIGxsb9PVlH5OhpqaGqakpubm5bNu2jS+++KLYOsu7pqpXr07NmjW5deuWTH0sLS1lhmTPnz+fqKgojh8/zv79+/njjz9em688kyZN4smTJzKbUvWioxrKU05uHreSM7B/aT6eBLCvoU30w5KtsKoggdr66kWGOAK0tzIiJimdOymZco4UKoOyigr17Rpw9sxp6b78/HzOnj2Ng2PjSizZa0gUMLe05WrEeemu/Px8rkacx8LWvtjD9m5dx65NvzN25s/UtSr+9/Zzj5IekJb6BD19I5SVlbGwtuVSmGzMi2HnsbYrPuabZGU9K3LDU0FRgfz8kvdiVvRnWFbxcvMLuP4gDSezF0OfJUATM12uJMhf6+Lyf08x1VXj5TNWS1+dpLTCxnFo3BMGrg1n8J8R0i3qfiqHoxJLW02hGFVtdWjREywIQoXz9fWlS5cuWFtbk5KSwrFjx6QNzjp16iCRSNi9ezfu7u6oq6ujpSX/2YKWlpbk5OSwdOlSunXrJrNgVnHi4+Pp0KEDa9eulRkSHRISwrx58+jevTuHDh1iy5Yt7NmzR24eqampeHp64uPjQ5cuXahVqxbNmjWjW7du9O7dGyicn3zixAm++uorVFVVX3sX3crKinXr1tG0aVOePn3K+PHjpY3L0rh58yZpaWncv3+fzMxM6fNr7ezsUFFR4dixY3z22WeMHj2aXr16cf/+faCwMffy4ljZ2dkMGjSIKVOmEBsby/Tp0/H29i52PnBOTg69e/cmLCyM3bt3k5eXJ83bwMAAFRUVateuLXPM88/UwsKCWrVqAfD111/j5+fHoEGDmDBhApcvX2bx4sUsWrRIetzZs2eJj4+nUaNGxMfHM2PGDPLz82UWQ9uxYweTJk2SDok2NjZGXV2d/fv3U6tWLdTU1NDV1cXPzw8fHx90dXXp3LkzWVlZXLhwgZSUFL7//nvCw8OZNm0aW7duxdXVlYULFzJ69GjatGlDvXr1is33VaqqqkV6wCUKikXSvY6mugoWZtWkr81NDXGwNiXlaQZ376eUKI9/rjzE+5M6xCRncDMxg64NqqGqpMCx/1/IalSrOiRn5LAhtPBGUm9HE24kppPwNAtNVUW+aFgdIy0VjlyX7SVSV1bA2VyPtefjS1Wnsq7fhxCzonkO8GLq/ybQoEFDGto78Oe6IDIzM+neo2e5xCuLc+rWox+rFs6krlV96lnbcfDvTWQ9e0arToXrI/y2YAb6htXoM3AkAHu2rGXHn78x9IeZGBnX5PGjwutTTV0dNXUNnmVmsHPDapq6tkNX35DEhHg2/74U4xq1aOjUEoBufb5h6dzpWNjUx8q2Ibu3bSDrWSbtOxfO313iPw0Do2p8813haKWcnBzu3SmcI5ubm0Ny0kNu34xGTV2DGqZmADR1bsW29b9TrboJZuYW3L4RxT9b1tOzV+9SndOK/gzLKt5fYf8xyc2KqIdpRN1Po3fjmqgrK7LvSuEUqf+5WZGYls2qkMK53jsj79PDsQY+beuyLSKBWvrqfNOsFtsiCheWzMzJ43ay7DSnzJz8IuscvI8+lN81H9JQ5rIgGsGCIFS4vLw8Ro4cyb1799DR0aFz587Sho6pqSl+fn5MnDhR+hzbwMBAufk4OjqycOFCAgICmDRpEq1bt8bf3/+1Q3xzcnKIjo4uMmd47NixXLhwAT8/P3R0dFi4cKH0MUyvGj16NJqamsyZMwcAe3t75syZw9ChQ3F2dsbU1JSZM2cydOhQLCwsyMrKoqCg+Lv/a9asYciQITRp0gQzMzPmzJnDuHHjXncK5Ro8eDDHjx+Xvm7cuPDO/e3btzE3NycoKIiMjAz8/f3x9/eXpmvTpo3MatkdOnTAysqK1q1bk5WVRb9+/ZgxY0axcePj49m1axcAjRo1knnv2LFjJX50kK6uLgcPHmTkyJE4OTlhZGTEtGnTZHqsnz17xpQpU7h16xZaWlq4u7uzbt06mUXUnjx5QnR0tPS1kpISS5YsYebMmUybNo1WrVoRHBzM4MGD0dDQYP78+YwfPx5NTU3s7e3x9fXl2bNnfPPNNwwcOJBu3boBMGTIEPbs2YOnpycnTpwoNt/y0MSuDgdXj5a+njeuFwDrdp1hyPQ/S5THqdsp6Kgp8VXjGuipKxP7KJPZB29KH3tkpKkiMxRRS1WRYa610VNXJi2rsCd5yp5o7j15JpOva93Cx5mdvCX/maoVVb8PIWZF69zFnZRHj1i+bAlJSYnY2NZn+crVGJbTcOiyOKctWnci9cljdvz5G09Skqldz5qxM39G9/8XrkpOfIBE8uKG3NG928nNzeGXObLrDXzx9WB6eHyHgoIC92JvEnJkLxnpqegZVKNh4+b09BwqfbyRa7tPefI4hU1//MrjlGTqWlgzJWCp9BnASQ/vI3lpmeKU5ETGDfla+nrXX+vY9dc6Gjg6MXNR4XPNB4/6gY2/r+C3n+fy9HEK+oZGdPqsF96jRlMaFf0ZllW8Y9eT0FNX4lvn2hhoqHAzMZ3xO65IR5IYa6uS/9LfxcS0bMbvuMrINnX53dOEpLQstoUnsOHCvTKtX2WoCr9rPkSSgtd9MxMEQagCzM3N8fX1lVlpWhDKk3pj7zcnKmNdfb6t0Hh7lvxeofEqQ2b4sgqNV9GPRdVvVvHX6dEtsyo0nrZ6xfYHWVaXP7LpY+K2NKRC4x0f41qh8aDif4dXxO+alnOPvzlRCZyZ2KZM8ilvoidYEARBEARBEAShCqtqw6HFwliCIAiCIAiCIAhClSF6ggVBqPJiY2MruwiCIAiCIAiVpop1BItGsCAIgiAIgiAIQlUmhkMLgiAIgiAIgiAIwkdK9AQLgiAIgiAIgiBUYVWsI1g0ggVBEARBEARBEKqyqjYcWjSCBUEQBEEQBEEQqjDRCBYEQRAE4aOzZ8nvlV2Ej456Y+8KjZcZvqxC41WGajqqFRqvloF6hcarCs6t3VixAce4Vmw84aMgGsGCIAiCIAiCIAhVWBXrCBaNYEEQBEEQBEEQhKqsqg2HFo9IEgRBEARBEARBEKoM0RMsCIIgCIIgCIJQhVWxjmDRCBYEQRAEQRAEQajKxHBoQRAEQRAEQRAEQfhIiUawIAilEhwcjEQi4fHjxxUeOzAwED09vXfORyKRsHPnznfO532PP3DgQLp3717ucd435ubm/Pzzz2WeVhAEQRA+VhJJ2WwfCtEIFgShVFxcXEhISEBXV/eNaSuzwfw6CQkJdOnSpcTpK6Oh5OPjg5OTE6qqqjRq1KjI+8HBwXzxxRfUqFEDTU1NGjVqxPr16985bk5ODhMmTMDe3h5NTU1q1qxJ//79+e+//+Smz8rKolGjRkgkEiIiImTeu3jxIq1atUJNTQ0zMzPmzZtXJNbMmTOxsLBATU0NR0dH9u/f/851OH/+PEOGDCnztJXJtYkFW38eyq2Ds8kMX0a3tg4i3gcWszLqWNHKqo67t2/Cq08Xundozpgh3xB99VKxae/cvsnsKWPx6tOFrq0asfOvP1+b919//k7XVo34bcm816Z7nU0b1tOlU3uaNbbH46s+XLp48a3zqurxxM/F+0NBIimT7UMhGsGCIJSKiooKJiYmZTp3JDs7u8zyKgkTExNUVVXLNM+8vDzy8/PLNM9vv/2Wvn37yn3v1KlTODg4sG3bNi5evIiXlxf9+/dn9+7d7xQzIyODsLAwpk6dSlhYGNu3byc6OprPP/9cbvoffviBmjVrFtn/9OlTPv30U+rUqUNoaCjz589nxowZ/Pbbb9I0U6ZMYeXKlSxdupSrV68ybNgwevToQXh4+DvVoVq1amhoaJR52sqkqa7Kpevx+PpvFvE+0JiVUceKVhZ1PHHkAKuWLeDrgUNZsnojdS2tmTp2BI9THslNn/XsGSY1TBk4dDT6Bkavzfv6tcvs37WVuhbWb12+/fv28tM8f4aOGMmmLTuwsbFl+NBBJCcnv3WeVTme+LkQKotoBAtCFde2bVtGjRqFr68v+vr6VK9enVWrVpGeno6Xlxfa2tpYWlqyb98+oGjv7p07d+jWrRv6+vpoamrSoEED9u7dS2xsLO3atQNAX18fiUTCwIEDpTG9vb3x9fXFyMgINzc3ABYuXCjtgTQzM2PEiBGkpaWVqj7m5ub8+OOP9OvXD01NTUxNTfnll19k0rw8HHnt2rVoaWlx48YN6fsjRozA1taWjIwM2rZty507dxgzZgwSiUTa+H8+NHvXrl3Y2dmhqqpKXFwc58+fp1OnThgZGaGrq0ubNm0ICwsrVR0AlixZwsiRI6lXr57c9//3v//x448/4uLigoWFBaNHj6Zz585s3769SFo/Pz+qVauGjo4Ow4YNe+1NB11dXQ4dOsSXX36JjY0NLVu2ZNmyZYSGhhIXFyeTdt++fRw8eJCffvqpSD7r168nOzub33//nQYNGvDVV1/h4+PDwoULpWnWrVvH//73P9zd3alXrx7Dhw/H3d2dBQsWFFs+FxcXJkyYILMvMTERZWVlTpw4Acj23BcUFDBjxgxq166NqqoqNWvWxMfHR3rsq738cXFxfPHFF2hpaaGjo8OXX37JgwcPpO/PmDGDRo0asW7dOszNzdHV1eWrr74iNTW12DKXhYMhV/Fbvptdx8q3B6iqxKuMmJVRx4pWFnXcsXkdnbv1pFPX7tSua4H3uCmoqalxcM9Ouemt6zdk0MjvadOxM8oqysXmm5mRwfyZ/2PUD9PQ0tZ+6/KtC/qDnr2/pHuPXlhYWjJluh9qamrs3L7trfOsyvHEz8X7QwyHFgShygkKCsLIyIhz584xatQohg8fTp8+fXBxcSEsLIxPP/0UT09PMjIyihw7cuRIsrKyOHHiBJcuXSIgIAAtLS3MzMzYtq3wj2Z0dDQJCQksXrxYJqaKigohISH8+uuvACgoKLBkyRKuXLlCUFAQR48e5Ycffii23LGxsUgkEoKDg2X2z58/H0dHR8LDw5k4cSKjR4/m0KFDcvPo378/7u7ueHh4kJuby549e1i9ejXr169HQ0OD7du3U6tWLWbOnElCQgIJCQnSYzMyMggICGD16tVcuXIFY2NjUlNTGTBgACdPnuTMmTNYWVnh7u5e7o0kgCdPnmBgYCCz78iRI1y7do3g4GA2btzI9u3b8fPzK3W+EolEZj72gwcP+O6771i3bp3cntTTp0/TunVrVFRUpPvc3NyIjo4mJSUFKBxKraamJnOcuro6J0+elL4ODAyUGXXg4eHBpk2bKCgokO7bvHkzNWvWpFWrVkXKsW3bNhYtWsTKlSu5ceMGO3fuxN7eXm498/Pz+eKLL3j06BHHjx/n0KFD3Lp1q0hvfExMDDt37mT37t3s3r2b48ePM3fuXLl5CoJQCgX53Lx+jUZOLaS7FBQUaNS0BVFX3q0BsWLRHJo5t6Jx05ZvnUdOdjbXrl6hpbOLTPlatnThYuS7jWCpivGE98vzG/3vun0oRCNYEAQcHR2ZMmUKVlZWTJo0CTU1NYyMjPjuu++wsrJi2rRpJCcnc1HOvKC4uDhcXV2xt7enXr16fPbZZ7Ru3RpFRUVpg8zY2BgTExOZecRWVlbMmzcPGxsbbGxsAPD19aVdu3aYm5vTvn17Zs2axV9//VVsuZWVlbGxsSnSCHN1dWXixIlYW1szatQoevfuzaJFi4rNZ+XKlSQkJODj48OgQYOYMWMGTk5OABgYGKCoqIi2tjYmJiaYmJhIj8vJyWH58uW4uLhIy9G+fXu++eYbbG1tqV+/Pr/99hsZGRkcP368BJ/E2/vrr784f/48Xl5eMvtVVFSkvbFdu3Zl5syZLFmypMRDt589e8aECRPo168fOjo6QGHv6sCBAxk2bBhNmzaVe9z9+/epXr26zL7nr+/fvw8UNooXLlzIjRs3yM/P59ChQ2zfvl3mRoOurq70+gD48ssv+e+//2Qayhs2bKBfv35y//jGxcVhYmJCx44dqV27Ns2bN+e7776TW+YjR45w6dIlNmzYgJOTEy1atGDt2rUcP36c8+fPS9Pl5+cTGBhIw4YNadWqFZ6enhw5cqTYc5iVlcXTp09ltoL8vGLTC0JVpViQTX5eHnoGhjL79fQNSUlOeut8jx/ez83rUQwc6vPmxK+R8jiFvLw8DA1ly2doaEhS0tuXr6rGE94vCpKy2d7GL7/8grm5OWpqarRo0YJz5869Nv3jx48ZOXIkNWrUQFVVFWtra/bu3Vu6+r5dUQVB+Jg4OLxYpEFRURFDQ0OZ3rLnjZeHDx8WOdbHx4dZs2bh6urK9OnT5TaU5XneyHzZ4cOH6dChA6ampmhra+Pp6UlycrLcHmgAU1NToqKiaN68ucx+Z2fnIq+vXbtWbFn09fVZs2YNK1aswMLCgokTJ5aoDioqKjLnDl70kFpZWaGrq4uOjg5paWlFhhI/16VLF7S0tNDS0qJBgwYlivuqY8eO4eXlxapVq4rk4ejoKHOTwNnZmbS0NO7evcv69eulsbW0tPj3339ljs3JyeHLL7+koKCAFStWSPcvXbqU1NRUJk2a9FblfW7x4sVYWVlha2uLiooK3t7eeHl5oaDw4k9Tjx49iIqKkr6uVq0an376qXQRsNu3b3P69Gk8PDzkxujTpw+ZmZnUq1eP7777jh07dpCbmys37bVr1zAzM8PMzEy6z87ODj09PZnrx9zcHO2XhlPWqFFD7s/Gc/7+/ujq6spsuQ9C33B2BEEoC4kP7vPbknmMnzoHlTJeC0IQhHe3efNmvv/+e6ZPn05YWBiOjo64ubkV+3c1OzubTp06ERsby9atW4mOjmbVqlWYmpqWKq5oBAuCgLKy7DwqiUQis+95D5u83sPBgwdz69YtPD09uXTpEk2bNmXp0qVvjKmpqSnzOjY2ls8++0y62FNoaKh0Lm9FLJx14sQJFBUVSUhIID09vUTHqKurF+l9HDBgABERESxevJhTp04RERGBoaFhsXVYvXo1ERERRERElPouJsDx48fp1q0bixYton///qU69vPPP5fGjoiIkOnVfd4AvnPnDocOHZL2AgMcPXqU06dPo6qqipKSEpaWlgA0bdqUAQMGAIWLj708lxaQvn7em16tWjV27txJeno6d+7cISoqCi0trWLnQT/n4eHB1q1bycnJYcOGDdjb2xc7xNnMzIzo6GiWL1+Ouro6I0aMoHXr1uTk5JTqXL1M3s/L63rWJ02axJMnT2Q2pepFbwIJQlWXJ1FBQVGRx49kF2F6nJKMvuHrF70qzs3oqzxOeYTP4H50a+tEt7ZOXIoIZdfWjXRr60ReXslHZejr6aOoqFhkkajk5GSMjN6ufFU5nvB+qazh0AsXLuS7777Dy8sLOzs7fv31VzQ0NPj999/lpv/999959OgRO3fuxNXVFXNzc9q0aYOjo2Op4opGsCAI78zMzIxhw4axfft2xo4dy6pVqwCk80FL8iUjNDSU/Px8FixYQMuWLbG2ti72sTxvcubMmSKv69evX2z6U6dOERAQwD///IOWlhbe3t4y76uoqJT4i1JISAg+Pj64u7vToEEDVFVVXzuMzNTUFEtLSywtLalTp06JYjwXHBxM165dCQgIKPYxP5GRkWRmZkpfnzlzRjpn+/miZ883dXV14EUD+MaNGxw+fLjI0LglS5YQGRlZpPG+efNmZs+eDRT2OJ84cUKmsXno0CFsbGzQ19eXyU9NTQ1TU1Nyc3PZtm0bX3zxxWvr/cUXX/Ds2TP279/Phg0biu0Ffk5dXZ1u3bqxZMkSgoODOX36NJcuFX3kSv369bl79y53796V7rt69SqPHz/Gzs7utTFeR1VVFR0dHZlNoqD41vkJwkdLooCldX0iQl8MhczPzyci9By2Dd7usTKOTVvwS9BWlv6+WbpZ2drRtpM7S3/fjKJiyX8WlVVUqG/XgLNnTsuU7+zZ0zg4Nn6r8lXleML7pawWxpI3BSgrK0tuzOzsbEJDQ+nYsaN0n4KCAh07duT06dNyj9m1axfOzs6MHDmS6tWr07BhQ+bMmVOqG1oASqVKLQiC8ApfX1+6dOmCtbU1KSkpHDt2TNrgrFOnDhKJhN27d+Pu7o66ujpaWlpy87G0tCQnJ4elS5fSrVs3mQWzihMfH0+HDh1Yu3atzJDokJAQ5s2bR/fu3Tl06BBbtmxhz549cvNITU3F09MTHx8funTpQq1atWjWrBndunWjd+/eQOHw1xMnTvDVV1+hqqr62jviVlZWrFu3jqZNm/L06VPGjx8vbVyWxs2bN0lLS+P+/ftkZmZKn8FrZ2eHiooKx44d47PPPmP06NH06tVLOs9WRUVFZnGs7OxsBg0axJQpU4iNjWX69Ol4e3vLDDl+WU5ODr179yYsLIzdu3eTl5cnzdvAwAAVFRVq164tc8zzz9TCwoJatWoB8PXXX+Pn58egQYOYMGECly9fZvHixTJzs8+ePUt8fDyNGjUiPj6eGTNmkJ+fL7MY2o4dO5g0aZLMkGhNTU26d+/O1KlTuXbtGv369Sv2PAYGBpKXl0eLFi3Q0NDgzz//RF1dXe4Nh44dO2Jvb4+Hhwc///wzubm5jBgxgjZt2hQ797miaKqrYGFWTfra3NQQB2tTUp5mcPd+ioj3AcSsjDpWtLKoY4++niycMxUrWzus6zfk7y3reZaZSSf3wptjC2ZNwdDImIHDCuf35uTkEBcbA0BuTi7JiQ+JuRGFuroGNWvVRkNDE/N6ljIx1NTU0dHVLbK/JDwHeDH1fxNo0KAhDe0d+HNdEJmZmXTv0bPUeYl44ufiY6njy/z9/YsswDl9+nRmzJhRJG1SUhJ5eXly1xB5+e/+y27dusXRo0fx8PBg79693Lx5kxEjRpCTk8P06dNLXE7RCBYE4Z3k5eUxcuRI7t27h46ODp07d5Y2dExNTfHz82PixInS59gGBgbKzcfR0ZGFCxcSEBDApEmTaN26Nf7+/q8d4puTk0N0dHSROcNjx47lwoUL+Pn5oaOjw8KFC6WPYXrV6NGj0dTUZM6cOQDY29szZ84chg4dirOzM6ampsycOZOhQ4diYWFBVlaWzMrEr1qzZg1DhgyhSZMmmJmZMWfOHMaNG/e6UyjX4MGDZRbTaty48C787du3MTc3JygoiIyMDPz9/fH395ema9Omjcxq2R06dMDKyorWrVuTlZVFv3795P4hei4+Pp5du3YB0KhRI5n3jh07Rtu2bUtUfl1dXQ4ePMjIkSNxcnLCyMiIadOmyfRYP3v2jClTpnDr1i20tLRwd3dn3bp1MqtQP3nyhOjo6CL5e3h44O7uTuvWrYs0yl+mp6fH3Llz+f7778nLy8Pe3p5//vmnSO82FA4F+/vvvxk1ahStW7dGQUGBzp07l2h4f3lrYleHg6tHS1/PG9cLgHW7zjBk+p8i3gcQszLqWNHKoo6tO7jx5HEKf65ZQcqjJOpZ2jDzp+Xo//9iWYkPEmSGXD5KeojPt19JX2/ftJbtm9Zi38iJuUvXlEW1ZHTu4k7Ko0csX7aEpKREbGzrs3zlagzLabjwxx5P/Fy8P3WUUDYrO0+aNInvv/9eZp9qGc7Hz8/Px9jYmN9++w1FRUWcnJyIj49n/vz5pWoESwpe921OEAThA2Nubo6vry++vr6VXRRBKJZ6Y+83JxKEV2SGL6vQeJVxnV46ML9C49UyKP1IHeH19JtV7HVT0T8XUPE/GxVRx89/O//mRCWwa0izEqfNzs5GQ0ODrVu30r17d+n+AQMG8PjxY/7+++8ix7Rp0wZlZWUOHz4s3bdv3z7c3d3JysqSeTTj64g5wYIgCIIgCIIgCEKFUlFRwcnJSeYxg/n5+Rw5cqTIkz6ec3V15ebNmzILUl6/fp0aNWqUuAEMohEsCIIgCIIgCIJQpVXW6tDff/89q1atIigoiGvXrjF8+HDS09Px8vICoH///jKPZBw+fDiPHj1i9OjRXL9+nT179jBnzhxGjhxZqrhiTrAgCB+V2NjYyi6CIAiCIAjCB+Ut2q9lom/fviQmJjJt2jTu379Po0aN2L9/v3SxrLi4OJnFPM3MzDhw4ABjxozBwcEBU1NTRo8ezYQJE0oVVzSCBUEQBEEQBEEQhErh7e1d5PGUz7282Odzzs7ORR6HWVqiESwIgiAIgiAIglCFKVRWV3AlEY1gQRAEQRAEQRCEKqyKtYFFI1gQBEEQBEEQBKEqe5tFrT5kYnVoQRAEQRAEQRAEocoQPcGCIAiCUMFSzi+r7CKUO/3Ocys0nqKBSYXGA0jaMLBC4z3LrdBwZIZX/HVa0XUUyl5F/34bueNahcYDsOj6RYXHLG9VrCO4bHuCr127Rr169coyS0EQBEEQBEEQBKEcKUgkZbJ9KMq0EZydnc2dO3fKMktBEARBEARBEARBKDOlGg79/fffv/b9xMTEdyqMIAiCIAiCIAiCULE+nD7cslGqRvDixYtp1KgROjo6ct9PS0srk0IJgiAIgiAIgiAIFaOqrQ5dqkawpaUlY8aM4ZtvvpH7fkREBE5OTmVSMEEQBEEQBEEQBEEoa6WaE9y0aVNCQ0OLfV8ikVBQUPDOhRIE4f0VHByMRCLh8ePHFR47MDAQPT29d85HIpGwc+fOd86nvOJX5jkuqco+h4IgCIIglB0FSdlsH4pS9QQvWLCArKysYt93dHQkPz//nQslCML7y8XFhYSEBHR1dd+YNjg4mHbt2pGSklImjdeykpCQgL6+fonTm5ub4+vri6+vb/kV6iM3Y8YMdu7cSURERJnlGRgYiK+v7xtvFpTXdVjR1/emDesJ+mMNSUmJWNvYMvF/U7F3cHjv4w39vAljvmxBdQNNLsU85Ptlh7gQnSA3rZKiAuP7OfPNpw2paaTN9buPmLL6GIfO35am+a5bY77r1pg61Qt/B127k8ScdSEcickAYIibLaO7NaS6njqX7jxi3O9nCY1JKrZ8I9ztGPypDWZGmiQ/zWLn2VimbwgjKydPmqaGvgY/fuNEp0amaKgqcet+KsOWnyz1uajoz7CyYlakqnBOP/Z45RmzdV19OloZoKOmRPyTLP66eJ87Kc/kpm1ZWxdPp5oy+3Ly8vHdFS03/VctauH1iTlGWipE309jzu4oLsc/lZv2j0FONKtrUGT/iehERqyLAKCjnTFfNq+FXU1t9DRU6LXsNNH3K26qaVUbDl2qnmATExPq1KlT4vQbN24kPT291IUSBOH9paKigomJSZn+sszOzi6zvErCxMQEVVXVMs0zLy/vvboJWNHnVChf+/ft5ad5/gwdMZJNW3ZgY2PL8KGDSE5Ofq/j9W5rS8Cw9sxedxLnYX9w8dZDds3tSzU9DbnpZ3i1ZvBnjfh+2SEaD1rF6t3hbJ7RE0fL6tI08YmpTF0djMuIQFxHBBIcfoctM3tRv5YevZzN8e/fDP+tEXwyYReX7zxi5+ROVNNRkxuvj2tdZn7thP+WCJzG7GTEryH0cq7LjH5NpGn0NFU4/KM7Obn59JxzmKZjdjJp7Xkep5fuZ6yiP8PKilmRqsI5/djjlWfMJqba9LQ3Zm9UEnOP3ebek2d4u9RGS0Wx2GMyc/KYtPe6dJt64KbcdJ0bVueHLjasOHaLPsvPEn0/lZUDm2CgqSw3/egNkbSZe1y6fbHkFLl5+Ry4/ECaRl1FkbA7j1lUTEyhbJXpI5JeNXToUB48ePDmhIIgVJq2bdsyatQofH190dfXp3r16qxatYr09HS8vLzQ1tbG0tKSffv2AUWH6t65c4du3bqhr6+PpqYmDRo0YO/evcTGxtKuXTsA9PX1kUgkDBw4UBrT29sbX19fjIyMcHNzA2DhwoXY29ujqamJmZkZI0aMKPWCe+bm5vz444/069cPTU1NTE1N+eWXX2TSvDyUd+3atWhpaXHjxg3p+yNGjMDW1paMjAzatm3LnTt3GDNmDBKJRNr4fz40e9euXdjZ2aGqqkpcXBznz5+nU6dOGBkZoaurS5s2bQgLCytVHV6VnJxMv379MDU1RUNDA3t7ezZu3CiTprhzumvXLqysrFBTU6Ndu3YEBQUVGWp98uRJWrVqhbq6OmZmZvj4+JT6BuaECROwtrZGQ0ODevXqMXXqVHJycqTnys/Pj8jISOk5DAwMBODx48cMHjyYatWqoaOjQ/v27YmMjJTmGxkZSbt27dDW1kZHRwcnJycuXLhAcHAwXl5ePHnyRJrnjBkzipTrdddhfn4+/v7+1K1bF3V1dRwdHdm6dSsABQUFdOzYETc3N+k0n0ePHlGrVi2mTZv22nzLw7qgP+jZ+0u69+iFhaUlU6b7oaamxs7t297reD69mvPH3kjWHbhEVFwyo37eT2ZWDgM6y+/h+bpjA+ZtOM2Bc7eITXjCqn/COXDuFqN7N5Om2XvmJgfO3SImPoWb8SnM+OMEaZnZNLOqhvdnDQg8cp0/g28SFf8En1WnyczOxbOdldx4LW2MORP9gC0ht4lLTOPoxf/YEnILJ0sjaZoxX9gTn5zO8BUhhMYkcef/091+kFqqc1HRn2FlxaxIVeGcfuzxyjNmB0tDTsU+5kzcE+6nZrMp4j7Zefk4m+sVe0xBATzNypNuqVl5ctP1d63D1gv32Bn2H7cS05m56xrPcvLo4WQqN/3TzFyS07Klm7OFIc9y8jn4UiP4n4gEfj12i9MxlXOTSiIpm+1DUa6NYDE/WBA+DEFBQRgZGXHu3DlGjRrF8OHD6dOnDy4uLoSFhfHpp5/i6elJRkZGkWNHjhxJVlYWJ06c4NKlSwQEBKClpYWZmRnbthX+AYuOjiYhIYHFixfLxFRRUSEkJIRff/0VAAUFBZYsWcKVK1cICgri6NGj/PDDD8WWOzY2FolEQnBwsMz++fPn4+joSHh4OBMnTmT06NEcOnRIbh79+/fH3d0dDw8PcnNz2bNnD6tXr2b9+vVoaGiwfft2atWqxcyZM0lISCAh4cUwzoyMDAICAli9ejVXrlzB2NiY1NRUBgwYwMmTJzlz5gxWVla4u7uTmlq6L8wve/bsGU5OTuzZs4fLly8zZMgQPD09OXfunEy6V8/p7du36d27N927dycyMpKhQ4cyefJkmWNiYmLo3LkzvXr14uLFi2zevJmTJ0/i7e1dqjJqa2sTGBjI1atXWbx4MatWrWLRokUA9O3bl7Fjx9KgQQPpOezbty8Affr04eHDh+zbt4/Q0FCaNGlChw4dePToEQAeHh7UqlWL8+fPExoaysSJE1FWVsbFxYWff/4ZHR0daZ7jxo0DCodem5ubA7z2OvT392ft2rX8+uuvXLlyRbrw4/Hjx5FIJAQFBXH+/HmWLFkCwLBhwzA1NWXatGlvvL7LUk52NteuXqGls4t0n4KCAi1bunAxMvy9jaespEBjaxOOhsVK9xUUwNGwWJrbyf+iqKKixLPsXJl9mVk5uDQ0k5teQUFCn7b10VRTJiwmkcb1DDl26cXPaEEBHLuUQHPranKPPxP9kEb1jHCyKGz0mhtr4da4FgfD70nTdG1qRtitJNaNacvtVX0JCejGwA7yG9XFqejPsLJiVqSqcE4/9njlGVNRAmZ6akQlvrihWwBEJaZTz0C92ONUlRT40c2SWW6WDG1ZixraKnLztqupzZmYRy/yLoAzMY9wNHvzVDGAnk412XfpPpk5788Isuc3lN91+1CUak6wIAgfJ0dHR6ZMmQLApEmTmDt3LkZGRnz33XcATJs2jRUrVnDx4sUix8bFxdGrVy/s7e0BqFevnvQ9A4PC+S/GxsZF5kxaWVkxb948mX0vz7k1Nzdn1qxZDBs2jOXLl8stt7KyMjY2NmhoyA6tdHV1ZeLEiQBYW1sTEhLCokWL6NSpk9x8Vq5ciYODAz4+Pmzfvp0ZM2ZIV7o3MDBAUVERbW1tTExMZI7Lyclh+fLlODo6Sve1b99eJs1vv/2Gnp4ex48f57PPPpMb/01MTU2lDTyAUaNGceDAAf766y+aN28u3f/qOZ04cSI2NjbMnz8fABsbGy5fvszs2bOlafz9/fHw8JCeeysrK5YsWUKbNm1YsWIFamryh5G+6vn1A4Wf3bhx49i0aRM//PAD6urqaGlpoaSkJHMOT548yblz53j48KF0ePpPP/3Ezp072bp1K0OGDCEuLo7x48dja2srLd9zurq6SCSSIp+LkZERFhYWACgqKsq9DrOyspgzZw6HDx/G2dkZKLx2T548ycqVK2nTpg2mpqasXLmS/v37c//+ffbu3Ut4eDhKSoV/Ol93fZellMcp5OXlYWhoKLPf0NCQ27dvvbfxjHQ1UFJU4GGK7KiChynp2JgZyj3m8IVb+PRuxslLd7n1XwrtGpvzxSc2KL6y2kqDutUIXuKJmooSaZnZ9J2xnaTUgsJ4jzNl4z3OxLqm/C+mW0JuY6SjxqEfuyBBgrKSAqsPRvHTjkvSNObG2gzuZMvSPVeYv+MiThZGzPdqQXZuyb+8VvRnWFkxK1JVOKcfe7zyjKmlqoSigqRIT27qszxMtORPh3qQls2fYQn89/QZakqKdLQyYGwbc2YdvsXjZy9uzmmpKqGkqEBymuyUiOS0bOoaab6xbA1NdbA20WbajqtvUbPy8yEtalUWRCNYEAQcXlp8QlFREUNDQ2mjFqB69cL5eA8fPizynHAfHx+GDx/OwYMH6dixI7169ZLJrzjyHqd2+PBh/P39iYqK4unTp+Tm5vLs2TMyMjKKNHShsHEYFRVVZP/zRs3Lr3/++ediy6Kvr8+aNWtwc3PDxcVF2oB+ExUVlSJ1ffDgAVOmTCE4OJiHDx+Sl5dHRkYGcXFxcvPo0qUL//77LwB16tThypUrRdLk5eUxZ84c/vrrL+Lj48nOziYrK6vIOXn1nEZHR9OsWTOZfS83mqFwuPHFixdZv369dF9BQQH5+fncvn2bHTt2MGfOHOl7V69epXbt2kXKuHnzZpYsWUJMTAxpaWnk5uYW+0z5l2OnpaUV+fKTmZlJTEwMAN9//z2DBw9m3bp1dOzYkT59+kgbuMXx9vZ+Y0/2zZs3ycjIKHJjJDs7m8aNG0tf9+nThx07djB37lxWrFgh0wgvqaysrCKLShYoqpb5vPSPwbhfDrP8+y5E/v4dBcCt/1JYe+BikeHT1+8m02Lo7+hqqtKjtS2rfvgMz0XHSx2vlZ0J43o4MGb1GS7cSKSeiQ7zvJozoZcDAdsKb/opKEBYTDJ+GwunNVyMfYRdbT0GdbJ55/oKgvD+uP0ok9uPXtxEu/Uog2kdLfikrj67ryWWWZyeTU25fj+12EW0hIohGsGCIKCsLLuQg0Qikdn3fHiLvIWfBg8ejJubG3v27OHgwYP4+/uzYMECRo0a9dqYmpqyd0tjY2P57LPPGD58OLNnz8bAwICTJ08yaNAgsrOz5TaCy9KJEydQVFQkISGB9PR0tLW133iMurp6kaE/AwYMIDk5mcWLF1OnTh1UVVVxdnYudqGq1atXk5lZ+Ef31c/hufnz57N48WJ+/vln6ZxpX1/fInm+ek5LIi0tjaFDh+Lj41Pkvdq1azNs2DC+/PJL6b6aNWsWSXf69Gk8PDzw8/PDzc0NXV1dNm3axIIFC94Yu0aNGkWGswPSntUZM2bw9ddfs2fPHvbt28f06dPZtGkTPXr0KF1F5cQG2LNnD6amskNzX26cZmRkEBoaiqKiosy88dLw9/fHz89PZt/kqdOZMm1GiY7X19NHUVGxyCIxycnJGBkZFXPU2yureElPMsjNy8dYX/a6NNbX5H6K/DnnSU8y+XL6dlSVFTHUUee/5DRmDW7L7YTHMulycvO59V/hvvAbD3CyqcFXreoVxtOTHeporKfOg1d6h5+b2rcxG0/EEHS08LO9cvcxGmpKLB3iwrztFykogPspmUTdk40ffe8JX7Qo+UKhFf0ZVlbMilQVzunHHq88Y6Zl5ZKXX4C2quwiWNpqijzNyi3mKFn5BXD3yTOqvbLYVVpWLrl5+RhqyQ6VNtRSISmt+KfoAKgrK9DFvjq/HIkpURkq0oc0lLkslOucYEEQqgYzMzOGDRvG9u3bGTt2LKtWrQIKe0qhsCfzTUJDQ8nPz2fBggW0bNkSa2tr/vvvv7cqz5kzZ4q8rl+/frHpT506RUBAAP/88w9aWlpFehFVVFRKVAeAkJAQfHx8cHd3p0GDBqiqqpKUVPzjWUxNTbG0tMTS0rLY1fdDQkL44osv+Oabb3B0dKRevXpcv379jWWxsbHhwoULMvvOnz8v87pJkyZcvXpVWoaXNxUVFQwMDGT2PR8K/LJTp05Rp04dJk+eTNOmTbGysuLOnTsyaeSdwyZNmnD//n2UlJSKxH75y4+1tTVjxozh4MGD9OzZkz/++KPYPOWRdx2+vJjZq7HNzF7MPx07diwKCgrs27ePJUuWcPTo0dfmK8+kSZN48uSJzDZ+wqQ3lvs5ZRUV6ts14OyZ09J9+fn5nD17GgfHxq858u2UVbyc3HzCr9+nXRNz6T6JBNo1rsO5q/GvPTYrJ4//ktNQUlSgeysbdp96/Q0IBYkEJSVFwm8l07ZhDZl4bRvW4Nx1+b046qqK5L+yfkl+fuFrCYVfCM9EPywynNqypg5xiSVfPK6iP8PKilmRqsI5/djjlWfMvAK4+/gZNtVe3ISTADbVNLn1SP5NsVdJgJo6qjx5pdGcVwBX/0ulRb0XjzySSKBFPQMi7z55bZ6fNqyOiqIC/0TcL3FdKoqkjLYPRbk2guvUqVNsz4YgCB8HX19fDhw4wO3btwkLC+PYsWPSBmedOnWQSCTs3r2bxMTE1670bGlpSU5ODkuXLuXWrVusW7dOumBWceLj47G1tS2yQFRISAjz5s3j+vXr/PLLL2zZsoXRo0fLzSM1NRVPT098fHzo0qUL69evZ/PmzdJVgqFwjuuJEyeIj49/bYMWCuesrlu3jmvXrnH27Fk8PDxQVy9+EY6SsLKy4tChQ5w6dYpr166VeOX9oUOHEhUVxYQJE7h+/Tp//fWXdFXm53d8J0yYwKlTp/D29iYiIoIbN27w999/l2phLCsrK+Li4ti0aRMxMTEsWbKEHTt2yKQxNzfn9u3bREREkJSURFZWFh07dsTZ2Znu3btz8OBBYmNjOXXqFJMnT+bChQtkZmbi7e1NcHAwd+7cISQkhPPnz0uvL3Nzc9LS0jhy5AhJSUnShduWLVtGhw4dpLHlXYfa2tqMGzeOMWPGEBQURExMDGFhYSxdupSgoCCgsJf4999/Z/369XTq1Inx48czYMAAUlJSis1XHlVVVXR0dGS20g6F9hzgxfatf7Fr5w5uxcQwa+YMMjMz6d6jZ6nyqeh4S7adw8vdEY9ODbGpbciS0W5oqKmwdn/hUOPVEz5j5qA20vTNbGvwxSfWmNfQxbVhLXb5f4mCgoSFm89K08wc1AZXezNqV9elQd1qzBzUhtaOtdn8bwzLdl9hYAdrvm5jgY2pLosHO6OhqsSfwYWN6N9GfiLz+KN9ofcY3MmG3i51qVNNi3b2NZjStzH7Qu9KG8fL9lyhmVU1xvWwp151bfq41sWrgzW/HSg6FaMizun7HrMiVYVz+rHHK8+YR24m42quR4vaulTXVuGrRiaoKipw5s5jAPo71eBzuxeL5nWxMcLWWBNDDWXMdNUY2LQmBhrKnIp9XCTvtSF36N3UlM8b16BeNU2mfl4fdRVFdoYW3ryf06sBvp0sixzX08mUo9cSeZKZU+Q9HXUlbEy0sDDWAqCukSY2JlpFepyFslGuw6EvX75cntkLgvAeyMvLY+TIkdy7dw8dHR06d+4sXRXY1NQUPz8/Jk6ciJeXF/3795c2wl7l6OjIwoULCQgIYNKkSbRu3Rp/f3/69+9fbOycnByio6OLrFo9duxYLly4gJ+fHzo6OixcuFD6yKBXjR49Gk1NTem8V3t7e+bMmcPQoUNxdnbG1NSUmTNnMnToUCwsLMjKynrtyvdr1qxhyJAhNGnSBDMzM+bMmSOzqNXbmDJlCrdu3cLNzQ0NDQ2GDBlC9+7defLk9Xec69aty9atWxk7diyLFy/G2dmZyZMnM3z4cGkjzMHBgePHjzN58mRatWpFQUEBFhYW0tWbS+Lzzz9nzJgxeHt7k5WVRdeuXZk6darMI4t69erF9u3badeuHY8fP+aPP/5g4MCB7N27l8mTJ+Pl5UViYiImJia0bt2a6tWrS4fI9e/fnwcPHmBkZETPnj2lQ4tdXFwYNmwYffv2JTk5menTpzNjxgySkpKkc4qh+Ovwxx9/pFq1avj7+3Pr1i309PRo0qQJ//vf/0hMTGTQoEHMmDGDJk0KG01+fn4cPHiQYcOGsXnz5lJd3++qcxd3Uh49YvmyJSQlJWJjW5/lK1djWE5DFMsq3tbgKIx0NZg2sBXV9TW5GPOQLyZt5uHjwp9ZM2Mdac8rgKqKEtO9WlO3hh5pmdkcOHeLQQG7eZL+YohhNT0N1kz4DBMDTZ6kZ3H5diLdJm7meOwzAIx01JjyZWOq66lzMfYRPeYc4uGTwvfMjLR4KRwB2yIpKChg6leNqWmgQdLTZ+wLvYvfxher0obFJNPvp6P4fe3ExF6NuPMwlQlB5/jr5C2Wj2hd4ee0NCojZkWqCuf0Y49XnjHD4lPRVn3IZ/Wroa2qSPyTLH45FSddLEtfXZmX/5xrqCjg0bgG2qqKZObkE/f4GQuOx3I/teh0pv2XH6CvqYJ3BwuMtFSJSkhlWFAYyf///PAaemoyv2sAzI00cDLX57s/QuWWt51tNWb3aih9/dNXhWshLD9aMUOnFarYcGhJQQmfY/T8OYgl8fzRFoIgCBXN3NwcX19fmZWmhRdmz57Nr7/+yt27dyu7KFXas5JNSfug6XeeW6HxFA1M3pyojCVtGFjhMSuSWiWsHFMVfjaEsjX2n2sVHvN46L03JypDl2fJf7pFWfrur7LpvFz1ZcM3J3oPlPjX2+tWVhUEQRDeT8uXL6dZs2YYGhoSEhLC/PnzS/0MYEEQBEEQhI9JiRvBAwYMKM9yCIIgCOXgxo0bzJo1i0ePHlG7dm3Gjh3LpEklX5RJEARBEISPX1VbHfqtB7rExMTwxx9/EBMTw+LFizE2Nmbfvn3Url2bBg0alGUZBUEQSiw2Nrayi/BeWbRokXSOtiAIgiAIgjxVrA38dqtDHz9+HHt7e86ePcv27dulK2JGRkYyffr0Mi2gIAiCIAiCIAiCIJSVt2oET5w4kVmzZnHo0CHpcxIB2rdvX+T5nIIgCIIgCIIgCML7S0EiKZPtQ/FWw6EvXbrEhg0biuw3NjZ+4zM0BUEQBEEQBEEQhPfHB9R+LRNv1ROsp6dHQkJCkf3h4eGYmpq+c6EEQRAEQRAEQRCEiiGRSMpk+1C8VSP4q6++YsKECdy/fx+JREJ+fj4hISGMGzeO/v37l3UZBUEQBEEQBEEQBKFMSAoKCgpKe1B2djYjR44kMDCQvLw8lJSUyMvL4+uvvyYwMBBFRcXyKKsgCIIgfBRazj1e4TEj/42s2ID/Xa/YeJXApG2XCo2XGJ9YofHyblyo0HgAmeHLKjTes9wKDSeUA7W3ftbN21Nv7F2h8Sri52LUjmtlks/SHvXLJJ/y9laXjYqKCqtWrWLq1KlcvnyZtLQ0GjdujJWVVVmXTxAEQRAEQRAEQShHH9JQ5rLwTvdOateuTe3atcuqLIIgCIIgCIIgCIJQrkrcCP7+++9LnOnChQvfqjCCIAiCIAiCIAhCxVKoWh3BJW8Eh4eHy7wOCwsjNzcXGxsbAK5fv46ioiJOTk5lW0JBEARBEARBEASh3IhGcDGOHTsm/f/ChQvR1tYmKCgIfX19AFJSUvDy8qJVq1ZlX0pBEARBEARBEARBKANv9YikBQsW4O/vL20AA+jr6zNr1iwWLFhQZoUTBEEQBEEQBEEQypd4TnAJPH36lMTEosv0JyYmkpqa+s6FEgShYgQHByORSHj8+HGFxw4MDERPT++d85FIJOzcufOd8ymv+JV5jkuqss9hWStNfT62uguCIAjC21CQlM32oXir1aF79OiBl5cXCxYsoHnz5gCcPXuW8ePH07NnzzItoCAI5cfFxYWEhAR0dXXfmDY4OJh27dqRkpJSJo3XspKQkCAzKuVNzM3N8fX1xdfXt/wK9ZGbMWMGO3fuJCIioszyDAwMxNfXt0xuFpTmmijt9VPWejWpyTctzDDQVOHmwzQWHLrJ1YTibyZrqSoyrHVd2toYoaOmzP2nz1h0OIbTtx4BsGN4C2roqhU5bmtoPJ7FPCd4aFcHxvRqQnV9DS7dTuL7X49z4foDuWmVFBUY/2VTvulQn5qGmly/l8KUwFMcCr3zFrV/wbWJBWP6d6SJXW1qVNPlyzG/8U/wxXfKs6JierrWYUj7elTTVuXaf0+Zsf0KkXFP5KbdOLIlLS0Ni+w/evUhg1adB2B+Pwd6NzeTef/4tYcM/O289PUQN1tGd2tIdT11Lt15xLjfzxIak1RsGUe42zH4UxvMjDRJfprFzrOxTN8QRlZOnjRNDX0NfvzGiU6NTNFQVeLW/VSGTHpI2NW4Ep+LyvgcK8OmDesJ+mMNSUmJWNvYMvF/U7F3cBDxPrCYFaWq/Fx8aN6qEfzrr78ybtw4vv76a3JycgozUlJi0KBBzJ8/v0wLKAhC+VFRUcHExKRM88zOzkZFRaVM83ydsi4/QF5eHhKJBAWFtxosU+Yq+px+6EpzTZTH9VNSHW2rMbq9BQEHrnPlv1S+ambKz33t6fvbeVIycoqkV1KQsOQrB1LSc/jfjqskpmVhoqNGalauNI1XYBgvX7YWRpos7efI0eiio7cAereyIuC7VoxadpTz0Q/w7t6IXT9+geOQdSQ+ySySfkb/lvRra8uIpUeIvpdCpyZ12Dy5K+3GbSHylvwYJaGprsql6/Gs/fs0mxcOeet8Kjpm10Y1mNy9PlO2XCbizmO+bVOXoKEt6OAfTHJadpH0w/4IRVnxxQekr6nM3nGt2BuRIJMu+NpDxm988SU5O/dFY7WXszn+/ZsxetVpLtxIZGRXO3ZO7kQT3x0kPn1WJGYf17rM/NqJ4StOcvZ6IpY1dFg54hMKCmDS2sKGtZ6mCod/dOfElQR6zjlM0tNnWNTQIeVpRqnOR2V8jhVt/769/DTPnynT/bC3d2T9uiCGDx3E37v3Y2hY9AaHiPd+xqxIH8rPxQc0krlMvNU3PA0NDZYvX05ycjLh4eGEh4fz6NEjli9fjqamZlmXURCEEmrbti2jRo3C19cXfX19qlevzqpVq0hPT8fLywttbW0sLS3Zt28fUHSo7p07d+jWrRv6+vpoamrSoEED9u7dS2xsLO3atQMK5/9LJBIGDhwojent7Y2vry9GRka4ubkBhQvo2dvbo6mpiZmZGSNGjCAtLa1U9TE3N+fHH3+kX79+aGpqYmpqyi+//CKT5uXhrGvXrkVLS4sbN25I3x8xYgS2trZkZGTQtm1b7ty5w5gxY2Tmrjwfmr1r1y7s7OxQVVUlLi6O8+fP06lTJ4yMjNDV1aVNmzaEhYWVqg6vSk5Opl+/fpiamqKhoYG9vT0bN26USVPcOd21axdWVlaoqanRrl07goKCigy1PnnyJK1atUJdXR0zMzN8fHxIT08vVRknTJiAtbU1Ghoa1KtXj6lTp0pveAYGBuLn50dkZKT0HAYGBgLw+PFjBg8eTLVq1dDR0aF9+/ZERr7ofYyMjKRdu3Zoa2ujo6ODk5MTFy5cIDg4GC8vL548eSLNc8aMGUXKlZ+fT61atVixYoXM/vDwcBQUFLhzp7A38uVrIjs7G29vb2rUqIGamhp16tTB399feuyrw6EvXbpE+/btUVdXx9DQkCFDhshctwMHDqR79+789NNP1KhRA0NDQ0aOHCk9P6XRr3kt/o5MYM+lB8QmZxCw/wbPcvL5zEF+w7ybgwk6asr8sP0KF+OfkvAki/C7T7j58MXn+zgzh0fpLzZXS0PupmQSVkzPpE+Pxvyx/zLrDl8j6u4jRi07SuazXAZ8aic3/dftbJn31wUOXLhD7P2nrNp7iQMXYhnds3Gp6/+ygyFX8Vu+m13HKq53pCxiDm5bl82n77L13D1uPkhj8pZLZGbn0aeFmdz0TzJySErNkm6fWBuRmZPH3kjZRnB2br5MuqeZL250eH/WgMAj1/kz+CZR8U/wWXWazOxcPNtZyY3Z0saYM9EP2BJym7jENI5e/I8tIbdwsjSSphnzhT3xyekMXxFCaEwSd/4/3e17xfcuy1MZn2NFWxf0Bz17f0n3Hr2wsLRkynQ/1NTU2Ll9m4j3AcWsSB/Kz4WCRFIm24finbo5NDU1MTAwwMDAQDR+BeE9ERQUhJGREefOnWPUqFEMHz6cPn364OLiQlhYGJ9++imenp5kZBS9wz9y5EiysrI4ceIEly5dIiAgAC0tLczMzNi2rfCPUXR0NAkJCSxevFgmpoqKCiEhIfz6668AKCgosGTJEq5cuUJQUBBHjx7lhx9+KLbcsbGxSCQSgoODZfbPnz8fR0dHwsPDmThxIqNHj+bQoUNy8+jfvz/u7u54eHiQm5vLnj17WL16NevXr0dDQ4Pt27dTq1YtZs6cSUJCAgkJL754ZmRkEBAQwOrVq7ly5QrGxsakpqYyYMAATp48yZkzZ7CyssLd3f2d1j549uwZTk5O7Nmzh8uXLzNkyBA8PT05d+6cTLpXz+nt27fp3bs33bt3JzIykqFDhzJ58mSZY2JiYujcuTO9evXi4sWLbN68mZMnT+Lt7V2qMmpraxMYGMjVq1dZvHgxq1atYtGiRQD07duXsWPH0qBBA+k57Nu3LwB9+vTh4cOH7Nu3j9DQUJo0aUKHDh149KhwqK6Hhwe1atXi/PnzhIaGMnHiRJSVlXFxceHnn39GR0dHmue4ceOAwqHX5ubmQOE11a9fPzZs2CBT3vXr1+Pq6kqdOnWK1GXJkiXs2rWLv/76i+joaNavXy/N71Xp6em4ubmhr6/P+fPn2bJlC4cPHy5y/o4dO0ZMTAzHjh0jKCiIwMBA6Y2AklJSkGBjos352BTpvgLgfGwK9qY6co9pZWXI5finjP/Ukr2jnFk/qCkDnGsXOwdLSUFC5wbV2X3xvtz3lZUUaGxpzNGIuy/KUABHI+7S3LaG3GNUlBV5lpMrsy8zOxcXu5qvqe3HSVlJkYa1dDl5/UVDsaAAQm4k0aSOXony+LKFGbvDE8jMzpPZ39LSkPMzO3JkUht+7N0QPQ3lwpiKCjSuZ8ixSy9+dxUUwLFLCTS3riY3xpnohzSqZ4STRWGj19xYC7fGtTgYfk+apmtTM8JuJbFuTFtur+pLSEA3BnaQ36iuynKys7l29QotnV2k+xQUFGjZ0oWLkeGvOVLEe59iCvIplNH2oXir4dD5+fnSlaCf3yHX1tZm7NixTJ48+b0ZQigIVZGjoyNTpkwBYNKkScydOxcjIyO+++47AKZNm8aKFSu4eLHoHcm4uDh69eqFvb09APXq1ZO+Z2BgAICxsXGROcFWVlbMmzdPZt/Lc27Nzc2ZNWsWw4YNY/ny5XLLraysjI2NDRoaGjL7XV1dmThxIgDW1taEhISwaNEiOnXqJDeflStX4uDggI+PD9u3b2fGjBnS55cbGBigqKiItrZ2kWGwOTk5LF++HEdHR+m+9u3by6T57bff0NPT4/jx43z22Wdy47+JqamptIEHMGrUKA4cOMBff/0lXWMBip7TiRMnYmNjI51yYmNjw+XLl5k9e7Y0jb+/Px4eHtJzb2VlxZIlS2jTpg0rVqxATa3oXFF5nl8/UPjZjRs3jk2bNvHDDz+grq6OlpYWSkpKMufw5MmTnDt3jocPH6KqqgrATz/9xM6dO9m6dStDhgwhLi6O8ePHY2trKy3fc7q6ukgkkiKfi5GRERYWFtLXHh4eLFiwgLi4OGrXrk1+fj6bNm2SKfPL4uLisLKy4pNPPkEikchtKD+3YcMGnj17xtq1a6U3dpctW0a3bt0ICAigevXqQOFoiGXLlqGoqIitrS1du3blyJEj0p+xV2VlZZGVlSWzT0elsJH6KF22BzklPQdzQ9mfgedq6qnjVEeNA1ceMOavS5jpqzPezQolBQlrQorOyW1jbYSWmhJ7LslvBBvpqKOkqMDDx7I3xB4+zsDGTP486cNhcfh0b8zJy/HcSnhCO0czvnC2QFGx6v3dN9LXQklRgaRU2c82KTULC+M3dww41tbFtqYOEzfL/i4+HpXIgYv3ufsok9qGGozvakPgkOb0XByCoY7q/39mskPVHz7OxLqm/LUdtoTcxkhHjUM/dkGCBGUlBVYfjOKnHZekacyNtRncyZale64wf8dFnCyMmO/Vgmfxt1j/z9mSnpKPXsrjFPLy8ooM0TU0NOT27Vsi3gcSUxDgLRvskydPZtmyZcydO1c6HHrOnDksXbqUqVOnlnUZBUEoBYeXFpJQVFTE0NBQ2qgFpF/kHz58WORYHx8fZs2ahaurK9OnT5fbUJbneSPzZYcPH6ZDhw6Ympqira2Np6cnycnJcnugobBxGBUVJdMQBHB2di7y+tq1a8WWRV9fnzVr1rBixQosLCykDeg3UVFRkTl3AA8ePOC7777DysoKXV1ddHR0SEtLIy5O/kIxXbp0QUtLCy0tLRo0aCA3TV5eHj/++CP29vYYGBigpaXFgQMHiuT56jmNjo6mWbNmMvtePVeRkZEEBgZKy6ClpYWbmxv5+fncvn2bOXPmyLxXXD02b96Mq6srJiYmaGlpMWXKlGLTvhw7LS0NQ0NDmRi3b98mJiYGgO+//57BgwfTsWNH5s6dK93/Ot7e3hw5ckT6ulGjRtSvX1/aG3z8+HEePnxInz595B4/cOBAIiIisLGxwcfHh4MHDxYb69q1azg6OsqMbHJ1dSU/P5/o6GjpvgYNGqCoqCh9XaNGDbk/T8/5+/ujq6srsz04vf2NdX+VggRS0rOZu/860Q/SOByVSOCpOHo0lt9r283BhDO3HpEkZ27q2xq38gQx/z0m8ldPnv7tzaLhbVl7+Br5+QVlFqOq+LKFGVH/PS2yiNbu8AQOX3lIdEIqhy4/YNDq8zjW0ZO7oFZJtLIzYVwPB8asPsMnE3bRb/5R3JrUYkKvF7/vFBQg4nYyfhvDuBj7iD+OXCfwyHW+6/3JO9VREIQPh0RSNtuH4q0awUFBQaxevZrhw4fj4OCAg4MDI0aMYNWqVaUeEiYIQtlSVlaWeS2RSGT2PZ8Hm5+fX+TYwYMHc+vWLTw9Pbl06RJNmzZl6dKlb4z56nSI2NhYPvvsMxwcHNi2bRuhoaHSubzZ2WX3hbw4J06cQFFRkYSEhBLPh1VXVy/yfLsBAwYQERHB4sWLOXXqFBERERgaGhZbh9WrVxMREUFERAR79+6Vm2b+/PksXryYCRMmcOzYMSIiInBzcyuS59tMMUlLS2Po0KHSMkRERBAZGcmNGzewsLBg2LBhMu/VrFl0COvp06fx8PDA3d2d3bt3Ex4ezuTJk9/4uaWlpVGjRg2Z/CMiIoiOjmb8+PFA4dDmK1eu0LVrV44ePYqdnR07duwodT09PDykjeANGzbQuXPnYhdPadKkCbdv3+bHH38kMzOTL7/8kt69e5c65svk/YzJ+3l6btKkSTx58kRmU3X8jNz8Agw0ZfPS11QmOV3+uU5Ky+ZuSiYvtzdjkzMw0lJF6ZUx0SY6qjQz1+fvV+aayuT3NJPcvHyM9WR7no31NLifIv9mVdLTTL6ctQfDXiuw8foDx6HrSH+Wze378uccf8ySUtLIzcvHSFtVZr+RtiqJT7OKOaqQuooinzWuyeazd1+bDuBucibJaVnU+f+VnQs/M3WZNMZ66jx4XHQhM4CpfRuz8UQMQUdvcOXuY/45H8eMjWGM7e4g/cJ6PyWTqHuPZY6LvvcEM5PKWzn9faSvp4+ioiLJycky+5OTkzEyMirmKBHvfYspyCfmBJfAo0ePpMPZXmZrayud+yUIwofJzMyMYcOGsX37dsaOHcuqVasApKsT5+Xlve5wAEJDQ8nPz2fBggW0bNkSa2tr/vvvv7cqz5kzZ4q8rl+/frHpT506RUBAAP/88w9aWlpF5nOqqKiUqA4AISEh+Pj44O7uToMGDVBVVSUpqfiFYkxNTbG0tMTS0rLYYbchISF88cUXfPPNNzg6OlKvXj2uX7/+xrLY2Nhw4cIFmX3nz5+Xed2kSROuXr0qLcPLm4qKCgYGBjL7lJSKzog5deoUderUYfLkyTRt2hQrKyvpglPPyTuHTZo04f79+ygpKRWJ/fIXGWtra8aMGcPBgwfp2bMnf/zxR7F5Fufrr7/m8uXLhIaGsnXrVjw8PF6bXkdHh759+7Jq1So2b97Mtm3b5P6tql+/PpGRkTI3TkJCQlBQUMDGxqZEZZNHVVUVHR0dmS1fQZno+6k0M3/RyJAAzerocyn+qdx8Lt57Si19dV7+imFmoE5iaha5r/TEfuZgQkpGNqduyn6xfFlObj7hNx/SrtGLRZwkEmjXyIxzUcU3ngGycvL4LzkdJUUFurtYsvtM1Ru2mJObx+V7T3C1fnF9SyTgYmVI2J3Hrz3W3bEGqkoK7LwQ/8Y4Jrpq6GuokPj0GTl5+YTfSqZtwxe9/xIJtG1Yg3PX5a/Ora6qSH6B7PXxvOde8v9X05noh0WGU1vW1CEuQXyne5myigr17Rpw9sxp6b78/HzOnj2Ng+O7LQ5XFeNVVkxBgLdsBDs6OrJs2bIi+5ctWyYzn04QhA+Lr68vBw4c4Pbt24SFhXHs2DFpg7NOnTpIJBJ2795NYmLia1d6trS0JCcnh6VLl3Lr1i3WrVsnXTCrOPHx8dja2hZZICokJIR58+Zx/fp1fvnlF7Zs2cLo0aPl5pGamoqnpyc+Pj506dKF9evXs3nzZrZu3SpNY25uzokTJ4iPj39tgxYK56yuW7eOa9eucfbsWTw8PFBXV3/tMW9iZWXFoUOHOHXqFNeuXWPo0KE8eCD/mawvGzp0KFFRUUyYMIHr16/z119/SUfePO/BnjBhAqdOncLb25uIiAhu3LjB33//XaqFsaysrIiLi2PTpk3ExMSwZMmSIr215ubm3L59m4iICJKSksjKyqJjx444OzvTvXt3Dh48SGxsLKdOnWLy5MlcuHCBzMxMvL29CQ4O5s6dO4SEhHD+/Hnp9WVubk5aWhpHjhwhKSlJOmx+2bJldOjQoUh8FxcXBg0aRF5eHp9//nmx9Vm4cCEbN24kKiqK69evs2XLFkxMTOQ+69rDwwM1NTUGDBjA5cuXOXbsGKNGjcLT01M6jaAsbTx3j88da+DesDrmhhr84GaFmooCe/5/Iatpn9kwvE1dafrt4f+ho6bE950sMdNXx8XCgIHOtdkWJnuDSQJ0tTdh76UH5L1hlPKSHeF4uTXAo4MtNmb6LBnZDg01JdYeugrA6u87MXPAiwVrmtlU5wsXC8xNdHBtUJNdM79AQUHCwm2h73QuNNVVcLA2xcHaFABzU0McrE3LtSeyLGKuDr7NVy3N6NnMFAtjLWb1boiGihJb/7+Hd8HXjozvWvQGSt+WZhy89IDHrzwKS0NFkUndbGlURw9TfXVcrAz5bVBT7iSlcyKq8PfVst1XGNjBmq/bWGBjqsviwc5oqCrxZ3Dhqvi/jfyEGf2aSPPcF3qPwZ1s6O1SlzrVtGhnX4MpfRuzL/SutHG8bM8VmllVY1wPe+pV16aPa128OlizcvOJUpzRyvkcK5rnAC+2b/2LXTt3cCsmhlkzZ5CZmUn3Hj1FvA8oZkX6UH4uqtpw6LdaGGvevHl07dqVw4cPS+frnT59mri4OOmjVwRB+PDk5eUxcuRI7t27h46ODp07d5auCmxqaoqfnx8TJ07Ey8uL/v37Fzv9wdHRkYULFxIQEMCkSZNo3bo1/v7+9O/fv9jYOTk5REdHF5kzPHbsWC5cuICfnx86OjosXLhQ+sigV40ePRpNTU3mzJkDgL29PXPmzGHo0KE4OztjamrKzJkzGTp0KBYWFmRlZVFQUHwrYc2aNQwZMoQmTZpgZmbGnDlzZBa1ehtTpkzh1q1buLm5oaGhwZAhQ+jevTtPnrx+OGndunXZunUrY8eOZfHixTg7OzN58mSGDx8uXYjKwcGB48ePM3nyZFq1akVBQQEWFhbS1ZtL4vPPP2fMmDF4e3uTlZVF165dmTp1qswji3r16sX27dtp164djx8/5o8//mDgwIHs3buXyZMn4+XlRWJiIiYmJrRu3Zrq1atLh7v179+fBw8eYGRkRM+ePfHz8wPAxcWFYcOG0bdvX5KTk5k+fTozZswgKSlJ7txhDw8PRowYQf/+/V97Y0JbW5t58+Zx48YNFBUVadasGXv37pW7gKOGhgYHDhxg9OjRNGvWDA0NDXr16sXChQtLfP5K43BUInoaynzXyhxDTRVuPExjzOZLPPr/hpGJjhovX54PU7MYvfkSvh0s+HNQUxJTs9h8IZ51Z2Tnazcz16eGrhr/FLMq9Mu2/nsDI111pn3Tkur6mly8lcgX0/6WLrxkVk1bphdRVVmJ6Z7O1DXRIS0zhwMXYhm04CBPihnCXVJN7OpwcPWLm1vzxvUCYN2uMwyZ/uc75V2eMfdEJGCopcL3na0x0lHlWvxTBq48J52HXVNfvUgvbL1qmjSrZ4DniqILTuUVFGBbU4eezWqho67Mw6fP+Dc6iYV7o8nOKxxyv+10LEY6akz5sjHV9dS5GPuIHnMO8fBJ4TOCzYy0ZIbMB2yLpKCggKlfNaamgQZJT5+xL/QufhtfrLwbFpNMv5+O4ve1ExN7NeLOw1QmBJ1j0z7Z0SdvUhmfY0Xr3MWdlEePWL5sCUlJidjY1mf5ytUYltPQ3Y89XmXFrEgfys9FcU8a+FhJCl73DfA14uPjWbFihXSBmvr16zNixAi5c8wEQRDehrm5Ob6+vjIrTQsvzJ49m19//ZW7d988r1B4v7Sce7zCY0b+G/nmRGXpvzcP8//QmbTtUqHxEuPlD3kuL3k3StcILguZ4UVHGpanZ7lvTiO839Teqkvv3ag3Lt2jB99VRfxczDh4o2zy+fTDeLzaW182hoaGfP7557Rs2VK6IMjz+WqvG5omCIIgvJ3ly5fTrFkzDA0NCQkJYf78+aV+BrAgCIIgCMKrPqRFrcrCWzWC9+/fT//+/UlOTi4ylFAikZR4cRNBEASh5G7cuMGsWbN49OgRtWvXZuzYsUyaNKmyiyUIgiAIwgeuirWB364RPGrUKPr06cO0adPKZbEQQRAEKHzUkvDCokWLpHO0BUEQBEEQhLfzVo3gBw8e8P3334sGsCAIgiAIgiAIwgeuqi2M9VaPSOrduzfBwcFlXBRBEARBEARBEAShoknK6N+H4q16gpctW0afPn34999/sbe3R1lZWeZ9Hx+fMimcIAiCIAiCIAiCUL6qWk/wWzWCN27cyMGDB1FTUyM4OBjJSzOpJRKJaAQLgiAIgiAIgiAI76W3agRPnjwZPz8/Jk6ciILCW42oFgRBEIQqK3LzlgqP6ezlUaHxTv/x8T8nODUltULjVcZzeyvax/j81ZdVhecSG30dWKHx0v4aWKHxPlaiJ7gEsrOz6du3r2gAC4IgCIIgCIIgfOAkVewZSW/Vih0wYACbN28u67IIgiAIgiAIgiAIQrl6q57gvLw85s2bx4EDB3BwcCiyMNbChQvLpHCCIAiCIAiCIAhC+RLDoUvg0qVLNG7cGIDLly/LvFfVutIFQRAEQRAEQRA+ZFWtCfdWjeBjx46VdTkEQRAEQRAEQRAEody9VSNYEARBEARBEARB+DgoVLGuYLG8syAIQgWaMWMGjRo1quxivNckEgk7d+4scfrAwED09PTKrTyCIAiC8LFTkJTN9jZ++eUXzM3NUVNTo0WLFpw7d65Ex23atAmJREL37t1LHVM0ggVBECrQuHHjOHLkSLnHCQ4ORiKR8Pjx41IdN2zYMCQSCT///HO5lKskEhIS6NKlS4nT9+3bl+vXX/9MWnNz83KpU3nl+yrXJhZs/Xkotw7OJjN8Gd3aOrxVPl84VGfDwMbsH9GCX75siG11rdem11RRxKdtXbYMcmL/yBYEeTaiRR096fv9mtZkeV97dg9rzrbBTZnZ1QYzPbVSl6us6lcZMb/tYEnYT924t6oPB6Z2onFdg9emH/qpNWf83bn7W28iF3zOrH6NUVV+8XVsdNf6HJrWidgVvbi2pDtrfT7B0kS71OX6kM/p+xqvsmzasJ4undrTrLE9Hl/14dLFix9EvCFutlxZ1pukPz05NrsrThZGr00/wt2OsJ97kPjnN0Qt78PcAc1QVVaUSVNDX4PVo1pxZ81XJP75DWd/+uKtylaRqsp1+rY2b97M999/z/Tp0wkLC8PR0RE3NzcePnz42uNiY2MZN24crVq1equ4ohEsCIJQgbS0tDA0NKzsYsi1Y8cOzpw5Q82aNSu1HCYmJqiqqpY4vbq6OsbGxuVYosqnqa7Kpevx+Pq//eMJ21oZMryVOWvP3mPopovEJGUQ8EV99NTlz4xSUpAwv4cdJtqqzNh7nQFrI1hw9BaJ6dnSNI6muvx98T7ef11i/M6rKClImNfdDjWl0n29KIv6lVZZxOze3Iwfv2rM/J2XaT/9AFfuPmbLuLYYacu/fnu1rMPUPo7M//sKLv/bx+jfz9G9eW2m9HrxpdjF1pg1R2/i9uMhes8PRllRgS3j2qKhoig3z/KsX2lVdMzKqGNF279vLz/N82foiJFs2rIDGxtbhg8dRHJy8nsdr5ezOf79m+G/NYJPJuzi8p1H7JzciWo68m+S9XGty8yvnfDfEoHTmJ2M+DWEXs51mdGviTSNnqYKh390Jyc3n55zDtN0zE4mrT3/TvWtCB/KdSqRlM1WWgsXLuS7777Dy8sLOzs7fv31VzQ0NPj999+LPSYvLw8PDw/8/PyoV6/eW9VXNIIFQfgoZGVl4ePjg7GxMWpqanzyySecP1/4x/F5r+iePXtwcHBATU2Nli1bFlnd/uTJk7Rq1Qp1dXXMzMzw8fEhPT1d+r65uTlz5szh22+/RVtbm9q1a/Pbb7/J5DFhwgSsra3R0NCgXr16TJ06lZycHOn7JRkOHRUVxSeffIKamhp2dnYcPnxYZohwbGwsEomETZs24eLigpqaGg0bNuT48ePS99u1aweAvr4+EomEgQMHvjZmfHw8o0aNYv369UUeeweFTwVo37496urqGBoaMmTIENLS0l6b5/O6/v7779SuXRstLS1GjBghfcyeiYkJxsbGzJ49W+Y4eXXdvn077dq1Q0NDA0dHR06fPi1N/6bh0G3btuXOnTuMGTMGiUQi8xSD133ma9euRUtLixs3bkjTjxgxAltbWzIyMl6bb1k7GHIVv+W72XXs7XuA+jSuwd7LD9l/LZE7jzJZdPQWWbn5dLGTfwOhi50xOmpKTN0TzZWEVB6kZnEx/im3kjKkaSb+fY0D1xKJfZTJraQMAg7fpLqOKtbGmhVev9Iqi5jD3WxZdzyGjSdvc/2/p4wNOk9mdi5ft5b/payZpSHnbiSx7cwd7ialE3zlPtvP3qFxvRc3xvouOM6mk7eJ/u8pV+4+xnv1WcyMNHE0f30Pc3nUr7QqOmZl1LGirQv6g569v6R7j15YWFoyZbofampq7Ny+7b2O5/1ZAwKPXOfP4JtExT/BZ9VpMrNz8WxnJTd9SxtjzkQ/YEvIbeIS0zh68T+2hNzCyfJF7/GYL+yJT05n+IoQQmOSuPP/6d53H8p1qoCkTLasrCyePn0qs2VlZcmNmZ2dTWhoKB07dnxRDgUFOnbsKPN3/lUzZ87E2NiYQYMGvUN9BUEQPgI//PAD27ZtIygoiLCwMCwtLXFzc+PRo0fSNOPHj2fBggWcP3+eatWq0a1bN2kDNSYmhs6dO9OrVy8uXrzI5s2bOXnyJN7e3jJxFixYQNOmTQkPD2fEiBEMHz6c6Oho6fva2toEBgZy9epVFi9ezKpVq1i0aFGx5X7eQI+NjQUK7252794dDQ0Nzp49y2+//cbkyZPlHjt+/HjGjh1LeHg4zs7OdOvWjeTkZMzMzNi2rfALS3R0NAkJCSxevLjYMuTn5+Pp6cn48eNp0KBBkffT09Nxc3NDX1+f8+fPs2XLFg4fPlzk3MgTExPDvn372L9/Pxs3bmTNmjV07dqVe/fucfz4cQICApgyZQpnz559bT6TJ09m3LhxREREYG1tTb9+/cjNzZWb9nnDOTg4GIDt27dTq1YtZs6cSUJCAgkJCdKyve4z79+/P+7u7nh4eJCbm8uePXtYvXo169evR0NDo9h830fKSopYG2sRevexdF8BEHr3MXY15A+1damnz5WEVEa3rcvWwU6s8XDk66amr53zpalS2Kv89Jn8z+ZjoqykiKO5PsevPpDuKyiA41ce0MxC/miP8zeTcTTXlw6ZrlNNk44ONTh8sfhrR0e98KZUyks98ELVkJOdzbWrV2jp7CLdp6CgQMuWLlyMDH9v4ykrKtC4niHHLr24rgsK4NilBJpbV5N7zJnohzSqZyQdMm1urIVb41ocDL8nTdO1qRlht5JYN6Ytt1f1JSSgGwM7yG9UC6VXVj3B/v7+6Orqymz+/v5yYyYlJZGXl0f16tVl9levXp379+/LPebkyZOsWbOGVatWvVN9xerQgiB88NLT01mxYgWBgYHSuaSrVq3i0KFDrFmzhmbNmgEwffp0OnXqBEBQUBC1atVix44dfPnll/j7++Ph4YGvry8AVlZWLFmyhDZt2rBixQrU1AqHcLm7uzNixAigsNd30aJFHDt2DBsbGwCmTJkiLZe5uTnjxo1j06ZN/PDDD3LLrqGhgY2NjbT39dChQ8TExBAcHIyJiQkAs2fPlpb7Zd7e3vTq1QuAFStWsH//ftasWcMPP/yAgUHhl2xjY+M3LhoVEBCAkpISPj4+ct/fsGEDz549Y+3atWhqFvbwLVu2jG7duhEQEFDkj9fL8vPz+f3339HW1sbOzo527doRHR3N3r17UVBQwMbGhoCAAI4dO0aLFi2KzWfcuHF07doVAD8/Pxo0aMDNmzextbUtklZZWRkbGxs0NDQAMDAwQFFREW1tbek5BUr0ma9cuRIHBwd8fHzYvn07M2bMwMnJ6bX5vo+M9LVQVJCQkpEjsz8lI4fa+upyj6mho0bjWqocjk5i0t9RmOqpMbptXZQUJKw9d69IegkwsrU5l/57SuyjzPKoxnvFSF8LJUUFEp88k9mf+PQZVjV05B6z7cwdDLRU2DO5AxIkKCsp8MfRG/y8+6rc9BIJzP66MWeuJxIV/6TM6yC831Iep5CXl1dkCo2hoSG3b996b+MZ6qiipKjAw8eyvwcePs7Euqau3GO2hNzGSEeNQz92kf5srD4YxU87LknTmBtrM7iTLUv3XGH+jos4WRgx36v4vxtC5Zg0aRLff/+9zL7STHF6ndTUVDw9PVm1ahVGRq+fY/4mohEsCMIHLyYmhpycHFxdXaX7lJWVad68OdeuXZM2gp2dnaXvGxgYYGNjw7Vr1wCIjIzk4sWLrF+/XpqmoKCA/Px8bt++Tf369QFwcHgxd08ikWBiYiKzeMPmzZtZsmQJMTExpKWlkZubi46O/C/EAM2bNycqKkr6Ojo6GjMzM5lGVfPmzeUe+3J9lJSUaNq0qbQ+8qxfv56hQ4dKX+/btw8NDQ0WL15MWFhYscN5r127hqOjo7QBDODq6kp+fj7R0dFUr14dLa0XCyx98803/Prrr0DhjQBt7Rc9jdWrV0dRUREFBQWZfW9aAOPl816jRg0AHj58KLcRbGpqKnNOi1OSz1xfX581a9bg5uaGi4sLEydOfGO+r8rKyioyFKwgPw+JQunmeFY0iQRSMnNYeDSG/AK4kZiOkZYKfZvUlNsIHt22LnUN1fHZeqUSSvthcLU1xrebHT+sDSX0VjJ1jbWY49GEsZ8/Y8GuoudtnqcTtrX06Dr7cCWUVhAqTis7E8b1cGDM6jNcuJFIPRMd5nk1Z0IvBwK2FQ4jVlCAsJhk/DaGAXAx9hF2tfUY3sWuMov+0XjblZ1fpaqqWuJGr5GREYqKijx48EBm/4MHD+TeXI6JiSE2NpZu3bpJ9+Xn5wOF34Oio6OxsLAoUWzRCBYEQQDS0tIYOnSo3N7Q2rVrS///6nxZiUQi/QV8+vRp6UINbm5u6OrqsmnTJhYsWFC+hS+hzz//XKa31dTUlJUrV/Lw4UOZOubl5TF27Fh+/vln6TDtN4mIiJD+/+VGv7zz9bpzWJyXj3neWH/TMW9S0s/8xIkTKCoqkpCQQHp6ukyjviT8/f3x8/OT2adYvRnKNeTf3CgPSSlp5OUXoK8he+71NZR59Erv8HOPMnLIzcsnv+DFvrhHmRhqqqCkICH3pTd82tSlZV19fLddISmtagzbTUpJIzcvn2q6sgv9VNNR4+ET+T3hE3vYs+VULH+eKOxVu3bvCZqqSiwY2IyF/1yh4KVzPfebJnzqaEo3/yMkpHz8PetCUfp6+igqKhZZlCo5Ofmde8HKM17y0yxy8/Ix1pMdZWKsp86Dx/Kv5al9G7PxRAxBRwvXYLhy9zEaakosHeLCvO0XKSiA+ymZRN17LHNc9D0xQqKsVMZzglVUVHBycuLIkSPSxxzl5+dz5MgRuVOubG1tuXTpksy+KVOmkJqayuLFizEzMytxbDEnWBCED56FhQUqKiqEhIRI9+Xk5HD+/Hns7F7cIT5z5oz0/ykpKVy/fl3aw9ukSROuXr2KpaVlkU1FRaVE5Th16hR16tRh8uTJNG3aFCsrK+7cuVOqutjY2HD37l2Zu6LPF/h61cv1yc3NJTQ0VFqf52XOy8uTptHW1papl7q6Op6enly8eJGIiAjpVrNmTcaPH8+BAwcAqF+/PpGRkTKLhIWEhEiHMwMy+b6PKzWrqKjInAso2Wd+6tQpAgIC+Oeff9DS0iryR1levq+aNGkST548kdmUqjuVbQXfICc3j+sP02hi9mIoogRoYqbL1YRUucdc/u8ppnpqvPy1qJaeGklp2UUawJ9YGDB2+1XuP5W/+MnHKCc3j8jYFFrbvZgOIJFAa7vqnI+Rv5Kuhqoir967yfv/cyl56UzP/aYJXZ1q0WPeUeKS0hGqJmUVFerbNeDsmRcLBOXn53P27GkcHBu/t/Fy8vIJv5VM24Y1pPskEmjbsAbnrifKPUZdVZH8l+8CAfmv/GyciX5YZDi1Zc3iR1oJH4bvv/+eVatWERQUxLVr1xg+fDjp6el4eXkBhetzTJo0CUC6EOjLm56eHtra2jRs2LDE39dANIIFQfgIaGpqMnz4cMaPH8/+/fu5evUq3333HRkZGTIrB86cOZMjR45w+fJlBg4ciJGRkfTO44QJEzh16hTe3t5ERERw48YN/v777xIt/vSclZUVcXFxbNq0iZiYGJYsWcKOHTtee8y5c+ewtbUlPj4egE6dOmFhYcGAAQO4ePEiISEh0nnGrw5X/uWXX9ixYwdRUVGMHDmSlJQUvv32WwDq1KmDRCJh9+7dJCYmFruSs6GhYZE/KMrKypiYmEgbuB4eHqipqTFgwAAuX77MsWPHGDVqFJ6enq+dD1xZ4uPjsbW15dy5c9J95ubmnDhxgvj4eJKSkoA3f+bP5x75+PjQpUsX1q9fz+bNm9m6detr832VqqoqOjo6Mltph0JrqqvgYG2Kg7VpYVxTQxysTTEz0S9xHlvCE+jaoDqf2lajtr46vu3qoaakyP6rhV9KJ3ayZLDLix7wXZceoK2mhHcbc2rpqdHCXI+vm5ny98UXi5WMbluXjrZGzDpwg4ycPPQ1lNHXUEZFsbSPSHr3+pVWWcRccSAKzzYW9HU1x6qGDj/1b4qGqhIb/y3s6f3luxZM6f1iKP+BiP/wam9Jjxa1qW2kSZsG1ZnY056DEf9JGwDzPJ3o42LO0F9Pk/YsF2NdNYx11VBTrvhrprQqOmZl1LGieQ7wYvvWv9i1cwe3YmKYNXMGmZmZdO/R872Ot2z3FQZ2sObrNhbYmOqyeLAzGqpK/Blc2NP728hPZB5/tC/0HoM72dDbpS51qmnRzr4GU/o2Zl/oXenPxrI9V2hmVY1xPeypV12bPq518epgXWZ1Ly8fynVaWY9I6tu3Lz/99BPTpk2jUaNGREREsH//fun3i7i4uHJZeFIMhxYE4aMwd+5c6SrHqampNG3alAMHDqCvry+TZvTo0dy4cYNGjRrxzz//SO8aOjg4cPz4cSZPnkyrVq0oKCjAwsKCvn37lrgMn3/+OWPGjMHb25usrCy6du3K1KlTmTFjRrHHZGRkEB0dLV2lWlFRkZ07dzJ48GCaNWtGvXr1mD9/Pt26dZMuzvVyfebOnUtERASWlpbs2rVLOmTN1NQUPz8/Jk6ciJeXF/379ycwMLDEdXmZhoYGBw4cYPTo0TRr1gwNDQ169erFwoUL3yq/8paTk0N0dDQZGS8e5TNz5kyGDh2KhYUFWVlZFBQUvPEzHz16NJqamsyZMwcAe3t75syZw9ChQ3F2dsbU1FRuvuWhiV0dDq4eLX09b1zhgmjrdp1hyPQ/S5RH8I1k9NSV8Wpphr6mMjGJ6Uz4+xopmYXXnrG2ikxPTGJaNhN2XmNEa3NWf+1IUno22yPusyk0XprmC4fCOVs/95JdVTzg0E2OV3D9SqssYu48dxdDbTUm9rDHWFeNy3GP+XJBMIn/3yNey1BTZjj5gl1XKCgoYFJPe2roq5OcmsWBiP+Yve3Fo1O+/f/VbndN6iATy3v1WdZcqNj6lVZFx6yMOla0zl3cSXn0iOXLlpCUlIiNbX2Wr1yNYTkMhy7LeNtOx2Kko8aULxtTXU+di7GP6DHnEA//fyE5MyMtmZ+NgG2RFBQUMPWrxtQ00CDp6TP2hd7Fb+OLVanDYpLp99NR/L52YmKvRtx5mMqEoHMsG+r6avj3yodynVbGcOjnvL29i+10eP6kh+K87XcbSUF5/cUWBEF4TwQHB9OuXTtSUlLeuFLy+ygkJIRPPvmEmzdvYmFhQWxsLHXr1iU8PPyNzxwW3k/qjUs+wqCsOHt5VGi803+sf3OiD5ym4ycVGi898mSFxqsKMsOXVWi8KvD0MIy+DqzQeGl/DazQeFDxv8Mr4jpdcy6uTPIZ1Lz2mxO9B0RPsCAIwntmx44daGlpYWVlxc2bNxk9ejSurq4lXvFQEARBEAShNCqxI7hSiEawIAjCeyY1NZUJEyYQFxeHkZERHTt2fG9WmBYEQRAE4eNT1RaKEo1gQRA+em3bti23uZrloX///vTv37/Y983NzT+o+giCIAiCILxPRCNYEARBEARBEAShCnv1CRQfO9EIFgRBEARBEARBqMKqVhNYNIIFQRAEQRAEQRCqtMp8RFJlqGpzoAVBEARBEARBEIQqTPQEC4IgCIIgCIIgVGFVqx9YNIIFQRAEoUo4/cf6yi7CRyc98mRlF0F4R+qNvSs0Xmb4sgqNB/Ast2Lj5d24ULEBGVjB8T5OVWw0tBgOLQiCIAiCIAiCIFQdoidYEARBEARBEAShChOPSBIEQRAEQRAEQRCqjKo2PLiq1VcQBEEQBEEQBEGowkRPsCAIgiAIgiAIQhVW1YZDi55gQRCEtzRjxgwaNWpU2cV4r0kkEnbu3Fni9IGBgejpkQi+FQABAABJREFU6b1TzIEDB9K9e/cyTysIgiAIHytJGW0fCtEIFgRBeEvjxo3jyJEj5R4nODgYiUTC48ePS3XcsGHDkEgk/Pzzz+VSrpJISEigS5cuJU7ft29frl+//k4xFy9eTGBgYJmnrUyuTSzY+vNQbh2cTWb4Mrq1dRDxPrCYH3u8yoj5scerLJs2rKdLp/Y0a2yPx1d9uHTxYrnFqgrntCrU8UMkGsGCIAhvSUtLC0NDw8ouhlw7duzgzJkz1KxZs1LLYWJigqqqaonTq6urY2xs/E4xdXV1S9ybXJq0lUlTXZVL1+Px9d8s4n2gMT/2eJUR82OPVxn279vLT/P8GTpiJJu27MDGxpbhQweRnJxcLvGqwjn9UOookUjKZPtQiEawIAjvnaysLHx8fDA2NkZNTY1PPvmE8+fPAy96Rffs2YODgwNqamq0bNmSy5cvy+Rx8uRJWrVqhbq6OmZmZvj4+JCeni5939zcnDlz5vDtt9+ira1N7dq1+e2332TymDBhAtbW1mhoaFCvXj2mTp1KTk6O9P2SDIeOiorik08+QU1NDTs7Ow4fPiwzRDg2NhaJRMKmTZtwcXFBTU2Nhg0bcvz4cen77dq1A0BfXx+JRMLAgQNfGzM+Pp5Ro0axfv16lJWVi7x/6dIl2rdvj7q6OoaGhgwZMoS0tLTX5vm8rr///ju1a9dGS0uLESNGkJeXx7x58zAxMcHY2JjZs2fLHCevrtu3b6ddu3ZoaGjg6OjI6dOnpenfNBz6f//7Hy1atCiy39HRkZkzZwJFhzhv3boVe3t7aX07duwovRZeTfu6aw9eXH9HjhyhadOmaGho4OLiQnR09GvP37s6GHIVv+W72XWs/HpkqlK8yoj5scerjJgfe7zKsC7oD3r2/pLuPXphYWnJlOl+qKmpsXP7tnKJVxXO6YdSR4Uy2j4UH1JZBUGoIn744Qe2bdtGUFAQYWFhWFpa4ubmxqNHj6Rpxo8fz4IFCzh//jzVqlWjW7du0gZqTEwMnTt3plevXly8eJHNmzdz8uRJvL29ZeIsWLCApk2bEh4ezogRIxg+fLhMY0ZbW5vAwECuXr3K4sWLWbVqFYsWLSq23M8bSLGxsQDk5eXRvXt3NDQ0OHv2LL/99huTJ0+We+z48eMZO3Ys4eHhODs7061bN5KTkzEzM2PbtsIvH9HR0SQkJLB48eJiy5Cfn4+npyfjx4+nQYMGRd5PT0/Hzc0NfX19zp8/z5YtWzh8+HCRcyNPTEwM+/btY//+/WzcuJE1a9bQtWtX7t27x/HjxwkICGDKlCmcPXv2tflMnjyZcePGERERgbW1Nf369SM3N1du2ucN5+DgYAA8PDw4d+4cMTEx0jRXrlzh4sWLfP3110WOT0hIoF+/fnz77bdcu3aN4OBgevbsSUFBgdx4Jbn2ntdhwYIFXLhwASUlJb799tvX1lkQBOF9l5OdzbWrV2jp7CLdp6CgQMuWLlyMDK/EkgkVQfQEC4IgVKL09HRWrFjB/Pnz6dKlC3Z2dqxatQp1dXXWrFkjTTd9+nQ6deqEvb09QUFBPHjwgB07dgDg7++Ph4cHvr6+WFlZ4eLiwpIlS1i7di3Pnj2T5uHu7s6IESOwtLRkwoQJGBkZcezYMen7U6ZMwcXFBXNzc7p168a4ceP466+/ii27hoYGNjY20t7XQ4cOERMTw9q1a3F0dOSTTz4p0lP6nLe3N7169aJ+/fqsWLECXV1d1qxZg6KiIgYGBgAYGxtjYmKCrq5usWUICAhASUkJHx8fue9v2LCBZ8+esXbtWho2bEj79u1ZtmwZ69at48GDB8XmC4UN7N9//x07Ozu6detGu3btiI6O5ueff8bGxgYvLy9sbGxkzqE848aNo2vXrlhbW+Pn58edO3e4efOm3LTKysrY2NigoaEBQIMGDXB0dGTDhg3SNOvXr6dFixZYWloWOT4hIYHc3Fx69uyJubk59vb2jBgxAi0trSJpS3rtAcyePZs2bdpgZ2fHxIkTOXXqlMy1JQiC8KFJeZxCXl5ekWk+hoaGJCUlVVKpBKF8iEawIAjvlZiYGHJycnB1dZXuU1ZWpnnz5ly7dk26z9nZWfp/AwMDbGxspO9HRkYSGBiIlpaWdHNzcyM/P5/bt29Lj3NweLE4hUQiwcTEhIcPH0r3bd68GVdXV0xMTNDS0mLKlCnExcUVW/bmzZsTFRWFqakpUNhza2ZmhomJiUwaeV6uj5KSEk2bNpWp76vWr18vU79///2X0NBQ6UJPxd2NvXbtGo6Ojmhqakr3ubq6kp+fL+0FfznfYcOGSdOZm5ujra0tfV29enXs7OxQUFCQ2ffyOZTn5fNeo0YNgGKPMTU1JSoqSua8eXh4SBvBBQUFbNy4EQ8PD7nHOzo60qFDB+zt7enTpw+rVq0iJSVFbtqSXnulrUNWVhZPnz6V2Qry8+SmFQRBEITKIFaHFgRB+MClpaUxdOhQIiIipFtkZCQ3btzAwsJCmu7V+bISiYT8/HwATp8+jYeHB+7u7uzevZvw8HAmT55MdnZ2hdalOJ9//rlM/Zo2bcq///7Lw4cPqV27NkpKSigpKXHnzh3Gjh2Lubl5ifN+Od/n82xB/vl63TkszsvHPG+sv+mYl/Xr14/o6GjCwsI4deoUd+/epW/fvnLTKioqcujQIfbt24ednR1Lly7FxsZG5mbI2yhNHfz9/dHV1ZXZch+EvlN8QRCEsqavp4+iomKRRbCSk5MxMjKqpFIJFUUiKZvtQyEawYIgvFcsLCxQUVEhJCREui8nJ4fz589jZ2cn3XfmzBnp/1NSUrh+/Tr169cHoEmTJly9ehVLS8sim4qKSonKcerUKerUqcPkyZNp2rQpVlZW3Llzp1R1sbGx4e7duzLDjF9eZOllL9cnNzeX0NBQaX2elzkv70Xvoba2tky91NXV8fT05OLFizKN2Jo1azJ+/HgOHDgAQP369YmMjJRZJCwkJAQFBQVsbGwAZPJ915Way0OtWrVo06YN69evZ/369XTq1Om15ZRIJLi6uuLn50d4eDgqKirSofMvK+m1V1qTJk3iyZMnMptSdae3zk8QBKE8KKuoUN+uAWfPvFisMD8/n7NnT+Pg2LgSSyYIZU+psgsgCILwMk1NTYYPH8748eMxMDCgdu3azJs3j4yMDAYNGkRkZCQAM2fOxNDQkOrVqzN58mSMjIykq/xOmDCBli1b4u3tzeDBg9HU1OTq1ascOnSIZcuWlagcVlZWxMXFsWnTJpo1a8aePXvkNpxedu7cOfr378+RI0cwNTWlU6dOWFhYMGDAAObNm0dqaipTpkwBKDJc+ZdffsHKyor69euzaNEiUlJSpIst1alTB4lEwu7du3F3d0ddXV3unFZDQ8Mic7mUlZUxMTGRNnA9PDyYPn06AwYMYMaMGSQmJjJq1Cg8PT2pXr16ic5NRYqPj6dDhw6sXbu2yJDo6dOnk52d/drFys6ePcuRI0f49NNPMTY25uzZsyQmJkpvMLzsTdfe21JVVS3ymCiJgmKp8tBUV8HCrJr0tbmpIQ7WpqQ8zeDuffnDu9/Fxx6vMmJ+7PEqI+bHHq8yeA7wYur/JtCgQUMa2jvw57ogMjMz6d6jZ7nEqwrn9EOpo8IHNZj53YlGsCAI7525c+dKVzlOTU2ladOmHDhwAH19fZk0o0eP5saNGzRq1Ih//vlH2mPq4ODA8ePHmTx5Mq1ataKgoAALC4tih8zK8/nnnzNmzBi8vb3Jysqia9euTJ06lRkzZhR7TEZGBtHR0dJVqhUVFdm5cyeDBw+mWbNm1KtXj/nz59OtWzfU1NSK1Hnu3LlERERgaWnJrl27pMPPTE1N8fPzY+LEiXh5edG/f38CAwNLXJeXaWhocODAAUaPHk2zZs3Q0NCgV69eLFy48K3yK285OTlER0eTkZEhs7937954e3ujqKgo84ijV+no6HDixAl+/vlnnj59Sp06dViwYAFdunSRm74k115laGJXh4OrR0tfzxvXC4B1u84wZPqfIt4HEPNjj1cZMT/2eJWhcxd3Uh49YvmyJSQlJWJjW5/lK1djWE7DoavCOf1Q6vghDWUuC5KC4p4TIQiC8B4KDg6mXbt2pKSkvPZ5su+rkJAQPvnkE27evImFhQWxsbHUrVuX8PDwNz5zWPh4qDd+8yOpBEH4+GSGl2w0Ull6Jv8JdOVGv1nF/n6rjHNa0b/DK6KOuy+//gkRJfVZw/dvVJk8oidYEAShHO3YsQMtLS2srKy4efMmo0ePxtXVVWaBLkEQBEEQhMokEcOhBUEQhLKSmprKhAkTiIuLw8jIiI4dO7JgwYLKLpYgCIIgCIJUVRsOLRrBgiB8UNq2bcuHNIujf//+9O/fv9j3zc3NP6j6CIIgCIIgfOhEI1gQBEEQBEEQBKEKE6tDC4IgCIIgCIIgCFWGGA4tCIIgCIIgCIIgVBlVrRGsUNkFEARBEARBEARBEISKInqCBUEQBEEQBEEQqjDxiCRBEARBEARBEMqcemPvCo+ZGb6swmMKHx6FqtUGFsOhBeH/2LvrsKi2rw/g3xl66C5pkLBQUQG9BsLFFvsqioh6jauYGNcAsQODaxegotiKhYGCiC3YCgaIAWKgCAgCc94/eDk/BgYEnQGR9fGZ53H2nHPW3lPM2nuffQghhBBCCCF1B40EE0IIIYQQQkgdRtOhCSGEEEIIIYTUGbQ6NCGEEEIIIYQQ8puikWBCCCGEEEIIqcNoOjQhhBBCCCGEkDqDVocmhBBSJ/j5+cHW1ramq0EIIYQQUq0oCSaEkDpq6tSpiIyMFHucqKgocDgcfPr0qUr7jR49GhwOB6tXr/7pOnA4HBw5cuSnj1Ndxy2tdTMzHFg9Cs/PLMTX+LXo3r4xxatlMX/3eDUR83ePV1Mxq9Pv3j6g9rSRI6J/tQUlwYQQUkcpKChAXV29pqsh1OHDh3H16lXo6enVdFV+CfJyMriX+BoTF++leLU05u8eryZi/u7xaipmdfrd2wfUnjZyOKK51RaUBBNCiIjl5eXB29sbWlpakJWVRZs2bXDjxg0A/xsVPXHiBBo3bgxZWVnY29vj/v37Ase4dOkS/vjjD8jJycHAwADe3t7Izs5mHzc2NsaiRYvg5eUFRUVFGBoaYvPmzQLHmD59OurXrw8ejwdTU1PMmTMH+fn57OOVmQ79+PFjtGnTBrKysrCxscG5c+cERj+Tk5PB4XAQFhYGR0dHyMrKomHDhoiOjmYf79ChAwBAVVUVHA4Hnp6eFcZ8/fo1xo8fj9DQUEhJSZV5/N69e3BycoKcnBzU1dXx999/Iysrq9zjGRsbAwB69eoFDofD3geAo0ePolmzZpCVlYWpqSnmzZuHgoICAIC/vz/09PTw4cMHdvuuXbuiQ4cO4PP5FR5X1M7EPsS89ccRfuGu2GLUpXg1EfN3j1cTMX/3eDUVszr97u0Dak8bOSK61RaUBBNCiIhNmzYNBw8eREhICOLi4mBubg5XV1d8/PiR3cbHxwcBAQG4ceMGNDU10b17dzZBffbsGTp16oQ+ffrg7t272Lt3Ly5duoRx48YJxAkICICdnR3i4+MxduxYjBkzBgkJCezjioqKCA4OxsOHD7FmzRps2bIFq1atKrfexQl6cnIyAKCwsBBubm7g8Xi4du0aNm/ejFmzZgnd18fHB1OmTEF8fDwcHBzQvXt3fPjwAQYGBjh48CAAICEhAampqVizZk25deDz+RgyZAh8fHzQoEGDMo9nZ2fD1dUVqqqquHHjBvbv349z584JPDel21HcAREUFITU1FT2fkxMDDw8PDBhwgQ8fPgQmzZtQnBwMBYuXAgAmDVrFoyNjTFixAgAwLp163D58mWEhISAy+WWe1xCCCGE/NooCSaEEBHKzs7Ghg0bsHz5cnTu3Bk2NjbYsmUL5OTksG3bNnY7X19fuLi4oFGjRggJCcHbt29x+PBhAMDixYvh7u6OiRMnwsLCAo6OjggMDMSOHTuQm5vLHqNLly4YO3YszM3NMX36dGhoaODChQvs47Nnz4ajoyOMjY3RvXt3TJ06Ffv27Su37jweD5aWluzo69mzZ/Hs2TPs2LEDTZo0QZs2bdgEsbRx48ahT58+sLa2xoYNG6CsrIxt27ZBQkICampqAAAtLS3o6OhAWVm53DosXboUkpKS8Pb2Fvr47t27kZubix07dqBhw4ZwcnLC2rVrsXPnTrx9+1ZoOzQ1NQEAKioq0NHRYe/PmzcPM2bMwNChQ2FqagoXFxfMnz8fmzZtAgBISEhg165diIyMxIwZM+Dj44N169bB0NCwwuOWlpeXh8zMTIEbwy8s9zkghBBCqhuXwxHJrbagSyQRQogIPXv2DPn5+WjdujVbJiUlhZYtW+LRo0do0aIFAMDBwYF9XE1NDZaWlnj06BEA4M6dO7h79y5CQ0PZbRiGAZ/PR1JSEqytrQEAjRv/b3ENDocDHR0dpKens2V79+5FYGAgnj17hqysLBQUFEBJSancurds2RKPHz9m7yckJMDAwAA6OjoC2whTsj2SkpKws7Nj2yNMaGgoRo0axd4/deoUeDwe1qxZg7i4OHDK+UP66NEjNGnSBPLy8mxZ69atwefzkZCQAG1t7TLtKM+dO3cQGxsrkNgXFhYiNzcXOTk57DTyFStWYNSoURgwYAAGDRr03eOWtnjxYsybN0+gTEK7BaR0hT+XhBBCSHWrPemraFASTAghv5isrCyMGjVK6Gho8SgkgDLny3I4HPD5fADAlStX4O7ujnnz5sHV1RXKysoICwtDQECAeCtfST169ECrVq3Y+/r6+ti0aRPS09MF2lhYWIgpU6Zg9erV7PRmUcnKysK8efPQu3fvMo/Jysqy/7948SIkJCSQnJyMgoICSEpW7U/nzJkzMXnyZIEyrT+m/1ilCSGEEPLTKAkmhBARMjMzg7S0NGJjY2FkZAQAyM/Px40bNzBx4kR2u6tXr7LJXkZGBhITE9kR3mbNmuHhw4cwNzf/4XpcvnwZRkZGAufwvnjxokrHsLS0xMuXL/H27Vtoa2sDQLnnvV69ehVt27YFABQUFODWrVvsebrS0tIAihLaYoqKilBUVBQ4xpAhQ+Ds7CxQ5urqiiFDhmDYsGEAAGtrawQHByM7O5sdDY6NjQWXy4WlpWW5bZGSkhKIDxQ9zwkJCRU+z3v37sWhQ4cQFRWF/v37Y/78+QKjusKOW5qMjAxkZGQEyjhciQr3IYQQQqpVHRsKpiSYEEJESF5eHmPGjIGPjw/U1NRgaGiIZcuWIScnB8OHD8edO3cAFK08rK6uDm1tbcyaNQsaGhpwc3MDULSqs729PcaNG4cRI0ZAXl4eDx8+xNmzZ7F27dpK1cPCwgIpKSkICwtDixYtcOLECfac4/Jcv34dHh4eiIyMhL6+PlxcXGBmZoahQ4di2bJl+PLlC2bPng0AZaYrr1u3DhYWFrC2tsaqVauQkZEBLy8vAICRkRE4HA6OHz+OLl26QE5ODgoKCmXiq6url7lkk5SUFHR0dNgE193dHb6+vhg6dCj8/Pzw7t07jB8/HkOGDGET9dLtAIpWiI6MjETr1q0hIyMDVVVVzJ07F926dYOhoSH69u0LLpeLO3fu4P79+1iwYAFevXqFMWPGYOnSpWjTpg2CgoLQrVs3dO7cGfb29uUeVxzk5aRhZvC/c46N9dXRuL4+MjJz8DItg+LVgpi/e7yaiPm7x6upmNXpd28fUHvaWJuu8SsKlAQTQoiILVmyhF3l+MuXL7Czs8Pp06cFEqQlS5ZgwoQJePLkCWxtbXHs2DF2xLRx48aIjo7GrFmz8Mcff4BhGJiZmWHAgAGVrkOPHj0wadIkjBs3Dnl5eejatSvmzJkDPz+/cvfJyclBQkICu0q1hIQEjhw5ghEjRqBFixYwNTXF8uXL0b17d4HpwsXtWbJkCW7fvg1zc3OEh4dDQ0MDQNFU5+JFqIYNGwYPDw8EBwdXui0l8Xg8nD59GhMmTECLFi3A4/HQp08frFy5stx2AEUraU+ePBlbtmyBvr4+kpOT4erqiuPHj8Pf3x9Lly6FlJQUrKysMGLECDAMA09PT7Rs2ZId0XZ1dcWYMWMwePBg3L59GwoKCkKPKw7NbIxwZusE9v6yqX0AADvDr+Jv310UrxbE/N3j1UTM3z1eTcWsTr97+4C60cbaiMMwDFPTlSCEkLoiKioKHTp0QEZGBlRUVGq6OlUWGxuLNm3a4OnTpzAzM0NycjJMTEwQHx//3WsOk/+Razru+xsRQogIfI2v3AwiUanu77fqbh/we7bx+vPPIjlOS9PyrwDxK6GRYEIIIeU6fPgwFBQUYGFhgadPn2LChAlo3bo1zMzMarpqhBBCCBGRujUZmpJgQgghFfjy5QumT5+OlJQUaGhowNnZ+ZdZYZoQQggh5EdQEkwIIdWoffv2qE1noXh4eMDDw6Pcx42NjWtVewghhBAiRB0bCqYkmBBCCCGEEELqMFodmhBCCCGEEEJIncGpWzkwuDVdAUIIIYQQQgghpLrQSDAhhBBCCCGE1GF1bCCYkmBCCCGEEEIIqdPqWBZMSTAhhBBSBziNLn+Vb3E4v3FHtcarCVu2zqjWeH2b1KvWeKotxlVrvJpw7/Tyao1XT02uWuMBQG5B9cb7Gr+2egMS8gPonGBCCCGEEEIIqcM4Ivr3I9atWwdjY2PIysqiVatWuH79ernbbtmyBX/88QdUVVWhqqoKZ2fnCrcvDyXBhBBCCCGEEFKHcTiiuVXV3r17MXnyZPj6+iIuLg5NmjSBq6sr0tPThW4fFRWFgQMH4sKFC7hy5QoMDAzw559/4vXr11WKS0kwIYQQQgghhJBqt3LlSowcORLDhg2DjY0NNm7cCB6Ph+3btwvdPjQ0FGPHjoWtrS2srKywdetW8Pl8REZGVikuJcGEEEIIIYQQUodxRHTLy8tDZmamwC0vL09ozG/fvuHWrVtwdnZmy7hcLpydnXHlypVK1TsnJwf5+flQU1OrUnspCSaEEEIIIYSQukxEWfDixYuhrKwscFu8eLHQkO/fv0dhYSG0tbUFyrW1tZGWllapak+fPh16enoCiXRlUBJMCCG/AT8/P9ja2tZ0NX4J7du3x8SJE0W+LSGEEEIqNnPmTHz+/FngNnPmTLHEWrJkCcLCwnD48GHIyspWaV+6RBIhhPwGpk6divHjx4s9TlRUFDp06ICMjAyoqKhUer/Ro0dj06ZNWLVqldiTzkOHDkFKSkrk29ak1s3MMMnDGc1sDKGrqYz+kzbjWNRdkRy7awMt9GmiC1U5KSR9yMHG2BdIfJdd7vby0hLwaFkPjsaqUJSVRPqXb9h8+QVuvvz8w3UQZ/vEHfPGmSO4cnwfsj5/hLahGToNHQ99cyuh2z66HoPYo7vx8e1r8AsLoaajD/su/dD4DxeB7d69foHIPVuQ8ugu+PxCaOgbod9EXwBVv0RS2O5QhARtw/v371Df0goz/p2DRo0bV/k4lVHdr6Mo4x0/FIaDe0KQ8fEDTMzqY/TE6bC0aSR02xdJT7Fr2wY8TXiI9LRUjBw/FW79B5d77H27tiNkUyB69hsEfz/fKtetOl/Dmoj3u6uJ77cf8aMrO5cmIyMDGRmZSm2roaEBCQkJvH37VqD87du30NHRqXDfFStWYMmSJTh37hwa/8D7k0aCCSHkN6CgoAB1dfWaroZQhw8fxtWrV6Gnp1ct8dTU1KCoqCjybWuSvJwM7iW+xsTFe0V63D/M1DDSwRC7b72G98H7SPqYg/ldLaEsK7yPXJLLwYKultBWkMGis0/xd9hdBF5Mwofsbz9VD3G1T9wxH1y5gLO7NqJtbw+MXLgR2oZm2L1kOrI/ZwjdXk5BEW3c3DFs3n/4e8kWNGnrivBNy/Dszg12m49v3yBk3gRo6BlgyJwA/L1kC/7oNRiSUtJVrl/EqZNYsWwxRo39B2H7D8PS0gpjRg3Hhw8ffrjNFanu11FU8S5GnsaWtQEY5DkKgVv3wMS8PuZMGYtPGR+Fbp+XmwsdXX14jpoAVTWNCo+d+Og+IsIPwMSs/g/Vrbpfw+qOVxfUxPfbj6iJ1aGlpaXRvHlzgUWtihe5cnBwKHe/ZcuWYf78+YiIiICdnd0PtZeSYEIIqUBeXh68vb2hpaUFWVlZtGnTBjduFP1gjYqKAofDwYkTJ9C4cWPIysrC3t4e9+/fFzjGpUuX8Mcff0BOTg4GBgbw9vZGdvb/RtqMjY2xaNEieHl5QVFREYaGhti8ebPAMaZPn4769euDx+PB1NQUc+bMQX5+Pvt4ZaZDP378GG3atIGsrCxsbGxw7tw5cDgcHDlyBACQnJwMDoeDsLAwODo6QlZWFg0bNkR0dDT7eIcOHQAAqqqq4HA48PT0rDDm69evMX78eISGhgodcb137x6cnJwgJycHdXV1/P3338jKyir3eIMGDcKAAQMEyvLz86GhoYEdO3YAKDvFef369bCwsICsrCy0tbXRt29f9rHS22ZkZMDDwwOqqqrg8Xjo3Lkznjx5wj4eHBwMFRUVnD59GtbW1lBQUECnTp2Qmppa4fPws87EPsS89ccRfkG0owe9Gukg4tE7nEt4j5efcrH2YjJyC/j400pT6PYulhpQlJHE/DNP8OhtFtKzvuF+6hckffz6U/UQV/vEHfPqyQNo2qELbNt3gmY9Y3QdPhFSMjK4HR0hdHtjG1tYtWgDTX0jqGnroVXnPtA2NEVKwv++My7s3QZz21ZwHjQKusYWUNPWg2VzR8grq1a5fjtDgtC7b3+49eoDM3NzzPadB1lZWRw5dPCH21yR6n4dRRXv8N6d6NS9N1y6usHQxAzjps6GrKwszpw4InT7+tYNMfyfyWjn3AlS0uXPJPmak4Pl/v9i/LS5UPjBzrbqfg2rO15dUBPfbz9CVAtjVdXkyZOxZcsWhISE4NGjRxgzZgyys7MxbNgwAICHh4fAdOqlS5dizpw52L59O4yNjZGWloa0tLQKfzsIQ0kwIYRUYNq0aTh48CBCQkIQFxcHc3NzuLq64uPH/40Q+Pj4ICAgADdu3ICmpia6d+/OJqjPnj1Dp06d0KdPH9y9exd79+7FpUuXMG7cOIE4AQEBsLOzQ3x8PMaOHYsxY8YgISGBfVxRURHBwcF4+PAh1qxZgy1btmDVqlXl1rs4QU9OTgYAFBYWws3NDTweD9euXcPmzZsxa9Ysofv6+PhgypQpiI+Ph4ODA7p3744PHz7AwMAABw8W/RBKSEhAamoq1qxZU24d+Hw+hgwZAh8fHzRo0KDM49nZ2XB1dYWqqipu3LiB/fv349y5cwLPTel2uLu749ixYwJ/7E6fPo2cnBz06tWrTIybN2/C29sb/v7+SEhIQEREBNq2bVtunT09PXHz5k2Eh4fjypUrYBgGXbp0EehwyMnJwYoVK7Bz505cvHgRKSkpmDp1arnH/FVJcjkw15TH7df/m8bMALj9KhNW2gpC92llrIrH6VkY28YIu4Y0xbp+DdG/qS64oplFV7swfKQmJcKkYTO2iMPlwqRhM7x68vD7uzMMku7H4UPqKxhZN/r/Q/Lx9PY1qOnUQ+ji6QgY3Qfb5vyDxzcuVbl6+d++4dHDB7B3cGTLuFwu7O0dcfdOfJWP97vKz8/H08RHsG3eii3jcrmwtWuFxw9+LmnZsGoRWjj8gaZ29j9Wt2p+Dek9Q2rCgAEDsGLFCsydOxe2tra4ffs2IiIi2MWyUlJSBDqaN2zYgG/fvqFv377Q1dVlbytWrKhSXDonmBBCypGdnY0NGzYgODgYnTt3BgBs2bIFZ8+exbZt29CiRQsAgK+vL1xcis7pCwkJQb169XD48GH0798fixcvhru7OzvaaGFhgcDAQLRr1w4bNmxgF3Lo0qULxo4dC6Bo1HfVqlW4cOECLC0tAQCzZ89m62VsbIypU6ciLCwM06ZNE1p3Ho8HS0tLdvT17NmzePbsGaKiotjzbBYuXMjWu6Rx48ahT58+AIr+2ERERGDbtm2YNm0aewkCLS2t754TvHTpUkhKSsLb21vo47t370Zubi527NgBeXl5AMDatWvRvXt3LF26FNra2mXa4erqCnl5eRw+fBhDhgxhj9OjRw+h05pTUlIgLy+Pbt26QVFREUZGRmjatKnQ+jx58gTh4eGIjY2Fo2PRj8DQ0FAYGBjgyJEj6NevH4CiH80bN26EmZkZ+3z5+/tX+Fz8ipRkJSHB5eDT1wKB8k9f82GgInyBER1FGWjrKSHq6Qf4nUqArrIsxrYxhgSXgz233lRHtX8ZEsw3MHw+FEqN0Morq+L9m5fl7pebk4XV/wxAYUE+OFwuugybANNGRdP5sjM/4VvuV1w+Fob2/Yah48CReHb3Bvav9oPH7ACgSeXPCc74lIHCwsIyp0moq6sjKel5FVr6e8v8nAF+YSFU1ASfJxVVdbx8kfzDx40+F4GniY+xenPoDx+jul9Des/UcTXYmTlu3LgygwPFoqKiBO4Xd4r/LEqCCSGkHM+ePUN+fj5at27NlklJSaFly5Z49OgRmwSXPG9FTU0NlpaWePToEQDgzp07uHv3LkJD//dDiGEY8Pl8JCUlwdraGgAEFnXgcDjQ0dFBeno6W7Z3714EBgbi2bNnyMrKQkFBAZSUlMqte8uWLfH48WP2fkJCAgwMDAQWmmjZsqXQfUu2R1JSEnZ2dmx7hAkNDcWoUaPY+6dOnQKPx8OaNWsQFxcHTjknCT169AhNmjRhE2AAaN26Nfh8PhISEqCtrV2mHZKSkujfvz9CQ0MxZMgQZGdn4+jRowgLCxMaw8XFBUZGRjA1NUWnTp3QqVMn9OrVCzweT2h9JCUl0arV/0aE1NXVBV5PoKiDoTgBBgBdXV2B16q0vLy8MtdIZPiF4HAlyt3nV8XlcPDpaz7+u5gEPgM8fZ8DdZ40+jTRqXNJ8I+SkeXh78Wb8S33K5IexOHMrg1Q0dKFsY0tGIYPAKjf3BH2XYqm7esYm+Nl4gPcOncM+KtrTVadVNK7t2nYHLgMC1ZuhHQlFwgipKaJamGs2oKmQxNCiBhlZWVh1KhRuH37Nnu7c+cOnjx5IpBIlT5flsPhgM8v+kF85coVuLu7o0uXLjh+/Dji4+Mxa9YsfPv2c4sRiUqPHj0E2mdnZ4eYmBikp6fD0NAQkpKSkJSUxIsXLzBlyhQYGxv/VDx3d3dERkYiPT0dR44cgZycHDp16iR0W0VFRcTFxWHPnj3Q1dXF3Llz0aRJE3z69OmH4wt7rRiGKXd7YddMLHh764fji0pmbgEK+QxU5AT7w1XkpJDxNV/oPh9zvuHN51zwSzT35aevUJOXhmQdmxNdyJEGh8tFVqlFsLI/Z0BBRa3c/ThcLtR09KFjbA6Hrv1h3bItYo/uAQDwFJXBlZCApr6RwD4a+ob4/KH8jhZhVFVUISEhUWZBow8fPkBDo+LFnOoSJWVVcCUk8Omj4PP0KeMDVNV/7Hl6mvAQnzI+wnvEQHRv3xzd2zfHvdu3EH5gD5o1tkFhYWGljlPdryG9Z0hdQkkwIYSUw8zMDNLS0oiNjWXL8vPzcePGDdjY2LBlV69eZf+fkZGBxMREdoS3WbNmePjwIczNzcvcpKUrt9rr5cuXYWRkhFmzZsHOzg4WFhZ48eJFldpiaWmJly9fClyGoHiBr9JKtqegoAC3bt1i21Nc55I/4hQVFQXaJScnhyFDhuDu3bsCybGenh58fHxw+vRpAIC1tTXu3LkjsEhYbGwsuFwuOw1cGEdHRxgYGGDv3r0IDQ1Fv379KrzMkaSkJJydnbFs2TLcvXsXycnJOH/+fJntrK2tUVBQgGvXrrFlHz58QEJCgsDrXVXCrpkoqd38h48nKgV8Bk/fZcNWX5kt4wCw1VfC47fCFxh5+DYLusqyAuMF+sqy+JD9DQX88jsCfkscLnRN6iP5wf/OlWT4fCQ9iEc9i8q/XxiGQWFBUaeDhKQU9Ewt8SFVcDr1x9RXUNbQrlL1pKSlYW3TANeuXmHL+Hw+rl27gsZNhJ8SUBdJSUnBvL41bt+6zpbx+XzcvnUdVg1+7LJATexaYV3IAfy3fS97s7CyQXuXLth78AgkJCo3C6S6X0N6z9RtNbE6dE2i6dCEEFIOeXl5jBkzBj4+PlBTU4OhoSGWLVuGnJwcDB8+HHfu3AEA+Pv7Q11dHdra2pg1axY0NDTg5uYGoOj8Xnt7e4wbNw4jRoyAvLw8Hj58iLNnz2Lt2rWVqoeFhQVSUlIQFhaGFi1a4MSJEzh8+HCF+1y/fh0eHh6IjIyEvr4+XFxcYGZmhqFDh2LZsmX48uULe55x6enK69atg4WFBaytrbFq1SpkZGTAy8sLAGBkZAQOh4Pjx4+jS5cukJOTg4JC2UWU1NXVy5xXJiUlBR0dHTbBdXd3h6+vL4YOHQo/Pz+8e/cO48ePx5AhQ9gFMUq3o9igQYOwceNGJCYm4sKFC+U+D8ePH8fz58/Rtm1bqKqq4uTJk+Dz+UKTbAsLC/Ts2RMjR47Epk2boKioiBkzZkBfXx89e/as8PmuiLBrJlZ1KrS8nDTMDP63YrOxvjoa19dHRmYOXqYJvxxPZRy+l4bJ7U3x5F02EtOz0LORDmSluDib8A4AMLmDKT5kf0PI9VcAgJMP0tG9gTZGtTZC+P230FeWQf+mejh2P+2H6yDO9ok7pn2Xvji6cSl0TetDz8wK108dRH5uLpq0cwUAHFm/BIpqGuj41wgAwKWju6FnWh+qWnooKMjH09vXcO/SWXTxmsAe06HbABwMnA9Dq8YwtrHFszs3kBh3BR6zV1a5jUOGDsOcf6ejQYOGaNioMXbtDMHXr1/h1qt3lY9VGdX9OooqXq8BQ7By0RxYWNmgvnVDHN0fityvX+HSpehzH7BgNtQ1tOA5umh9g/z8fKQkPwMAFOQX4MO7dDx78hhycjzo1TMEjycPY1NzgRiysnJQUlaGhUXVLpVU3a9hdcerC2ri++1H1KL8VSQoCSaEkAosWbKEXeX4y5cvsLOzw+nTp6GqqiqwzYQJE/DkyRPY2tri2LFj7Ihp48aNER0djVmzZuGPP/4AwzAwMzMrc5mfivTo0QOTJk3CuHHjkJeXh65du2LOnDnw8/Mrd5+cnBwkJCSwqxpLSEjgyJEjGDFiBFq0aAFTU1MsX74c3bt3ZxfnKtmeJUuW4Pbt2zA3N0d4eDg7FU5fXx/z5s3DjBkzMGzYMHh4eCA4OLjSbSmJx+Ph9OnTmDBhAlq0aAEej4c+ffpg5cr//dgv3Y5i7u7uWLhwIYyMjATO2S5NRUUFhw4dgp+fH3Jzc2FhYYE9e/YIXa0aAIKCgjBhwgR069YN3759Q9u2bXHy5MkKR5qrQzMbI5zZ+r9EadnUooXLdoZfxd++u374uDHPPkJZVhKD7fShypPC8/c5mHsygV0sS1NBWmCq9/vsb5hzMgEjHQyxrm9DfMj+hvD7aThw++cuESWu9ok7ZgOHDsjJ/IzoA8HI+pQBbSMzDJqxBArKRdOhMz+kg1Nimnh+Xi5ObQ9E5sd3kJSWgYaeAdzGzkQDhw7sNlYt2qDr8ImIPboHp0PWQl3PAP0m+sHQqlGV29ipcxdkfPyI9WsD8f79O1haWWP9pq1QF9PU1up+HUUVr21HV3z+lIFd2zYg4+N7mJpbwn/Feqj+/2JZ796mCnQWfnyfDm+vv9j7h8J24FDYDjSybY4l/2372WYJqO7XsLrj1QU18f1Gvo/DVHQiEyGEkHJFRUWhQ4cOyMjI+O5Kyb+i2NhYtGnTBk+fPoWZmRmSk5NhYmKC+Pj4715zmPwcuabCV8EUJ6fRHtUa7/zGHdUaryZs2TqjWuP1rcLq0KKg2qL636fV7d7p5dUar56aXLXGqwmydWCIrbq/w7/GV27m2M94lJr9/Y0qwVpX/vsb/QLqwNuUEEIIABw+fBgKCgqwsLDA06dPMWHCBLRu3VpggS5CCCGE1D11bXVoSoIJIaSO+PLlC6ZPn46UlBRoaGjA2dkZAQEBNV0tQgghhJBqRUkwIYT8oPbt21d4aZxfjYeHBzw8yp8Sa2xsXKvaQwghhBDRqE0rO4sCJcGEEEIIIYQQUofVsRyYkmBCCCGEEEIIqdPqWBbMrekKEEIIIYQQQggh1YVGggkhhBBCCCGkDqPVoQkhhBBCCCGE1Bl1bWEsDkNLgRJCCCFExOSajqvWeF/j11ZrvLrgn8OPqj3mul7W1R6TEAI8Tf8qkuOYa8mJ5DjiRiPBhBBCCCGEEFKH1bGBYEqCCSGEEEIIIaROq2NZMK0OTQghhBBCCCGkzqCRYEIIIYQQQgipw2h1aEIIIYQQQgghdUZdWx2apkMTQgghhBBCCKkzKAn+ScbGxli9enVNV+OX4efnB1tbW/a+p6cn3Nzcaqw+P6M2172uad++PSZOnCjybUn1ioqKAofDwadPn2q6KoQQQkidwhHRrbaodUlwTf2ADQ4OhoqKSrXHrQ6lE9ffnYmJCc6dO1frf3D36NEDhoaGkJWVha6uLoYMGYI3b96Uu/3Hjx8xfvx4WFpaQk5ODoaGhvD29sbnz5+/G2vfvn2wtbUFj8eDkZERli9fXmabqKgoNGvWDDIyMjA3N0dwcLDA456enuBwOOxNXV0dnTp1wt27d4XG/PbtGzQ0NLBkyRKhj8+fPx/a2trIz8/HoUOHMH/+/O+2A0CVtv0Zou5Eqc4Ot+99NoKDgwVeS2G35OTkaqlrZYjrs17bv0N+Ja2bmeHA6lF4fmYhvsavRff2jWu6SqQCbU1U4f+nGVb3sIRPO2MYqcqWu629oTLW9bIWuK3uYVmNtSWEVEody4JrXRJcF3z79q2mq/Dbunv3LjIyMtCuXbuarspP69ChA/bt24eEhAQcPHgQz549Q9++fcvd/s2bN3jz5g1WrFiB+/fvIzg4GBERERg+fHiFcU6dOgV3d3eMHj0a9+/fx/r167Fq1SqsXbuW3SYpKQldu3ZFhw4dcPv2bUycOBEjRozA6dOnBY7VqVMnpKamIjU1FZGRkZCUlES3bt2ExpWWlsbgwYMRFBRU5jGGYRAcHAwPDw9ISUlBTU0NioqKFbajWFW2rW0KCwvB5/PFHmfAgAHs65iamgoHBweMHDlSoMzAwKBKx8zPzxdTbUltIC8ng3uJrzFx8d6argr5jmb6iujdSAsnH7/HkgtJePU5F+McDaEgLVHuPl/zCzHzZCJ7m3P6aTXWmBBSGRwR/astalUS7OnpiejoaKxZs0ZgtCE6OhotW7aEjIwMdHV1MWPGDBQUFLD7ffnyBe7u7pCXl4euri5WrVpVZkQ5Ly8PU6dOhb6+PuTl5dGqVStERUUBKOrtHzZsGD5//szG9fPzY/fNycmBl5cXFBUVYWhoiM2bNwvU++XLl+jfvz9UVFSgpqaGnj17CoySFI8YLVy4EHp6erC0FN5DWjxiu337dhgaGkJBQQFjx45FYWEhli1bBh0dHWhpaWHhwoUC+3369AkjRoyApqYmlJSU4OTkhDt37gAoGtGZN28e7ty5w7ateARv5cqVaNSoEeTl5WFgYICxY8ciKyuriq+aoIiICLRp0wYqKipQV1dHt27d8OzZM/bx5ORkcDgc7Nu3D3/88Qfk5OTQokULJCYm4saNG7Czs4OCggI6d+6Md+/esfvduHEDLi4u0NDQgLKyMtq1a4e4uLgy8Y8ePYpOnTpBSkqq1tW9tEmTJsHe3h5GRkZwdHTEjBkzcPXq1XKTiYYNG+LgwYPo3r07zMzM4OTkhIULF+LYsWMCn5fSdu7cCTc3N4wePRqmpqbo2rUrZs6ciaVLl4JhGADAxo0bYWJigoCAAFhbW2PcuHHo27cvVq1aJXAsGRkZ6OjoQEdHB7a2tpgxYwZevnwp8HyUNHz4cCQmJuLSpUsC5dHR0Xj+/DmbwJf+PK9fvx4WFhaQlZWFtra2QOdA6W0zMjLg4eEBVVVV8Hg8dO7cGU+ePGEfL54Fcvr0aVhbW0NBQYFN5quiffv28Pb2xrRp06CmpgYdHR2B7xGGYeDn5wdDQ0PIyMhAT08P3t7e7L4vXrzApEmT2M9pybqFh4fDxsYGMjIySElJETpjxs3NDZ6enuz9vLw8TJ8+HQYGBuzo/bZt25CcnIwOHToAAFRVVcHhcAT2AwA5OTn2ddTR0YG0tDR4PB57397eHv/995/APra2tgLt5XA42LBhA3r06AF5eXmB763Y2Fg0btwYsrKysLe3x/379wWOdfDgQTRo0AAyMjIwNjZGQEBAuc97Re3h8/lYvHgxTExMICcnhyZNmuDAgQPs6+Hs7AxXV1f2ff7x40fUq1cPc+fOrdTzRCrvTOxDzFt/HOEXhM8MIb+OjubquJz8CVdTPiPtyzeE3U7Dt0I+HIxVyt2HYYDMvEL29iWvsPoqTAghQtSqJHjNmjVlRhykpKTQpUsXtGjRAnfu3MGGDRuwbds2LFiwgN1v8uTJiI2NRXh4OM6ePYuYmJgySca4ceNw5coVhIWF4e7du+jXrx86deqEJ0+ewNHREatXr4aSkhIbd+rUqey+AQEBsLOzQ3x8PMaOHYsxY8YgISEBQNHohqurKxQVFRETE4PY2Fj2R3TJEd/IyEgkJCTg7NmzOH78OICiH76lf1Q9e/YMp06dQkREBPbs2YNt27aha9euePXqFaKjo7F06VLMnj0b165dY/fp168f0tPTcerUKdy6dQvNmjVDx44d8fHjRwwYMABTpkxBgwYN2LYNGDAAAMDlchEYGIgHDx4gJCQE58+fx7Rp037qNczOzsbkyZNx8+ZNREZGgsvlolevXmVGr3x9fTF79mzExcVBUlISgwYNwrRp07BmzRrExMTg6dOnmDt3Lrv9ly9fMHToUFy6dAlXr16FhYUFunTpgi9fvggcNzw8HD179qyVda/Ix48fERoaCkdHxyol+J8/f4aSkhIkJctfKD4vLw+ysoJT3eTk5PDq1Su8ePECAHDlyhU4OzsLbOPq6oorV66Ue9ysrCzs2rUL5ubmUFdXF7pNo0aN0KJFC2zfvl2gPCgoCI6OjrCysiqzz82bN+Ht7Q1/f38kJCQgIiICbdu2Lbcenp6euHnzJsLDw3HlyhUwDIMuXboIdCbk5ORgxYoV2LlzJy5evIiUlBSB74DKCgkJgby8PK5du4Zly5bB398fZ8+eBVCU2K1atQqbNm3CkydPcOTIETRq1AhA0RTuevXqwd/fn/2clqzb0qVLsXXrVjx48ABaWlqVqouHhwf27NmDwMBAPHr0CJs2bYKCggIMDAxw8OBBAEBCQgJSU1OxZs2aKre1Mvz8/NCrVy/cu3cPXl5ebLmPjw8CAgJw48YNaGpqonv37uzrcevWLfTv3x9//fUX7t27Bz8/P8yZM0dg+r2fnx+MjY0BoML2LF68GDt27MDGjRvx4MEDTJo0CYMHD0Z0dDQ4HA5CQkJw48YNBAYGAgBGjx4NfX19zJ07t1qfJ0J+FRIcwEBFFo/fZbNlDIDH77JhqiZX7n4yklzMdzXHAldzjLKvB11F6WqoLSGkKjgc0dxqi1p1iSRlZWWBEQcAmDVrFgwMDLB27VpwOBxYWVnhzZs3mD59OubOnYvs7GyEhIRg9+7d6NixI4CiH9B6enrscVNSUhAUFISUlBS2fOrUqYiIiEBQUBAWLVoEZWVlcDgcNm5JXbp0wdixYwEA06dPx6pVq3DhwgVYWlpi79694PP52Lp1Kzt6ExQUBBUVFURFReHPP/8EAMjLy2Pr1q2Qlv7fHwZDQ0Po6uoKxOLz+di+fTsUFRVhY2ODDh06ICEhASdPngSXy4WlpSWWLl2KCxcuoFWrVrh06RKuX7+O9PR0yMjIAABWrFiBI0eO4MCBA/j777+hoKAASUnJMm0rOZJkbGyMBQsWYPTo0Vi/fn3VX7z/16dPH4H727dvh6amJh4+fIiGDRuy5VOnToWrqysAYMKECRg4cCAiIyPRunVrAEUjhCV/9Do5OQkcd/PmzVBRUUF0dDQ73fb169e4e/cuOnfuXOvqXp7p06dj7dq1yMnJgb29PduBUhnv37/H/Pnz8ffff1e4naurKyZNmgRPT0906NABT58+ZUfeUlNTYWxsjLS0NGhrawvsp62tjczMTHz9+hVyckU/jo4fPw4FBQUARZ0Kurq6OH78OLjc8vvjhg8fjqlTpyIwMBAKCgr48uULDhw4wCYmpaWkpEBeXh7dunWDoqIijIyM0LRpU6HbPnnyBOHh4YiNjYWjoyMAIDQ0FAYGBjhy5Aj69esHoKgza+PGjTAzMwNQ1Gnm7+9f4fMmTOPGjeHr6wsAsLCwwNq1axEZGQkXFxekpKRAR0cHzs7OkJKSgqGhIVq2bAmgaAq3hIQEFBUVy3xO8/PzsX79ejRp0qTS9UhMTMS+fftw9uxZtvPC1NSUfVxNTQ0AoKWlJda1EAYNGoRhw4ax958/fw6gqCPJxcUFQFHHQb169XD48GH0798fK1euRMeOHTFnzhwAQP369fHw4UMsX76c7TTU0NBgXysJCQmh7cnLy8OiRYtw7tw5ODg4ACh6Di5duoRNmzahXbt20NfXx6ZNm+Dh4YG0tDScPHkS8fHxbKdRZZ+nvLw85OXlCZTJyMiw38mE1BYKMpKQ4HLKjOR+yS2EjoLw9/PbrG/YFZeKN5m5kJWUgLOFGqa0M8aCc8/xKbf8WUiEkOpVi/JXkahVI8HCPHr0CA4ODmyCCQCtW7dGVlYWXr16hefPnyM/P5/9MQkUJdMlpxzfu3cPhYWFqF+/PhQUFNhbdHS0wHTX8jRu/L8FPIoT5fT0dADAnTt38PTpUygqKrLHVVNTQ25ursCxGzVqJJAAA8COHTuwePFigTJjY2OB8xm1tbVhY2MjkERoa2sLxM/KyoK6urpA25KSkr7btnPnzqFjx47Q19eHoqIihgwZgg8fPiAnJwcABI43evTo7z5PQFHSMXDgQJiamkJJSYkdrUlJSRHYruRzWpxcFY+KlW4jALx9+xYjR46EhYUFlJWVoaSkhKysLIHjhoeHs9OZf0RN1X306NECz3VJPj4+iI+Px5kzZyAhIQEPDw926mZFMjMz0bVrV9jY2AhMUW3QoAEbp7izYOTIkRg3bhy6desGaWlp2Nvb46+//gKACpNXYYrPGb59+zauX78OV1dXdO7cmR1R7ty5Mxu/QYMGAICBAweisLAQ+/btAwDs3bsXXC6XnbFQmouLC4yMjGBqaoohQ4YgNDSUfc+W9ujRI0hKSqJVq1Zsmbq6OiwtLfHo0SO2jMfjsUkVAOjq6rKvYUxMjMDrExoaWm77S743Sh+nX79++Pr1K0xNTTFy5EgcPny4wmnqxaSlpcsc93tu374NCQmJKp0bHxoaKtDOmJiYKsUUxs7OTmh5cVIKFCWaJV+PR48esR1KxVq3bo0nT56gsLDoh/m4ceMQGRlZYeynT58iJycHLi4uAu3asWOHwHdjv3790KtXLyxZsgQrVqyAhYVFldu5ePFiKCsrC9xKf7cT8rtK+vgV119+xqvPeXj6IQebr71CVl4h2pio1nTVCCF1WK0aCRaXrKwsSEhI4NatW5CQEFzYoXTSIUzp6accDoedIpuVlYXmzZsL/WGsqanJ/l9eXr5SdRUW63vxdXV12fObS6ooGUxOTka3bt0wZswYLFy4EGpqarh06RKGDx+Ob9++gcfj4fbt2+z2SkpKlap/9+7dYWRkhC1btkBPTw98Ph8NGzYssxhYyTYVd3CULis5DXno0KH48OED1qxZAyMjI8jIyMDBwUHguOHh4ejRo0el6vkr1d3f37/cqbcaGhrQ0NBA/fr1YW1tDQMDA1y9elUgiSjty5cv6NSpExQVFXH48GGBup08eZKddlo8esvhcLB06VIsWrQIaWlp0NTUZBOM4tFDHR0dvH37ViDO27dvoaSkxB4HKHqfm5ubs/e3bt0KZWVlbNmyBQsWLMDWrVvx9etXgedMSUkJffv2RVBQELy8vBAUFIT+/fuX+9lUVFREXFwcoqKicObMGcydOxd+fn64cePGD3eACPuMFXc22NnZCXwWSo+If+84xe8FAwMDJCQk4Ny5czh79izGjh2L5cuXIzo6usIp7nJycgKdgEBR50TpzpCS07tLviaV1aNHD4HOAn19/XK3/V78YpX93hOH4vUNTpw4UaYtJUdoc3Jy2L8NJc8Vr4qZM2di8uTJ5cYgpLbIyitAIZ+BoozgbyVFWQlk5lVuVJfPAC8/50JTvuprcxBCxKc2TWUWhVqXBEtLS7O9/QBgbW2NgwcPgmEY9odgbGwsFBUVUa9ePaiqqkJKSgo3btyAoaEhgKLzIBMTE9nzBJs2bYrCwkKkp6fjjz/+qFTcymrWrBn27t0LLS2tSieKotSsWTOkpaVBUlKSHbksTVjbbt26BT6fj4CAAHa0r3gkrljJZKYyPnz4gISEBGzZsoV9nksvePSjYmNjsX79enTp0gVA0WJk79+/Zx/PysrChQsXsGHDhh86fk3WXUtLq1LneRYnU6WnXZaUmZkJV1dXyMjIIDw8vMy5vkZGRuXuKyEhwSYLe/bsgYODA9uR4+DggJMnTwpsf/bs2QqTcaAoCeRyuWziW15iNXz4cLRv3x7Hjx/H5cuXhV6iqSRJSUk4OzvD2dkZvr6+UFFRwfnz59G7d2+B7aytrVFQUIBr166x06GLX2sbG5sKYxSTk5Or8mehomN1794d3bt3xz///AMrKyvcu3cPzZo1q9J3kKampsB5w4WFhbh//z67kFOjRo3A5/MRHR1d5lxuAOyslJLxFBUVK72qdun4mZmZSEpKqtS+AHD16lX2+zojIwOJiYmwtrYGUPSaxcbGCmwfGxuL+vXrl+nErKg9JRcSq2hEfMqUKeByuTh16hS6dOmCrl27sqcwCDuuMDT1mfwuChng5adcWGrK425qUUcSB4Clpjyin2dU6hgcAHpKMnjw9ucW2iSEiFrdyoJr3XRoY2NjXLt2DcnJyXj//j3Gjh2Lly9fYvz48Xj8+DGOHj0KX19fTJ48GVwuF4qKihg6dCh8fHxw4cIFPHjwAMOHDweXy2WT5vr168Pd3R0eHh44dOgQkpKScP36dSxevBgnTpxg42ZlZSEyMhLv378vd3plae7u7tDQ0EDPnj0RExODpKQkREVFwdvbG69evapwXw8PD8ycOfOnni9nZ2c4ODjAzc0NZ86cQXJyMi5fvoxZs2bh5s2bbNuSkpJw+/ZtvH//Hnl5eTA3N0d+fj7+++8/PH/+HDt37sTGjRt/qi6qqqpQV1fH5s2b8fTpU5w/f77M6MiPsrCwwM6dO/Ho0SNcu3YN7u7uAqNdERERqF+/vtCOgHv37rFTdG/fvs2unP2r1F2Ya9euYe3atbh9+zZevHiB8+fPY+DAgTAzM2MTz9evX8PKygrXr18HUJSI/Pnnn8jOzsa2bduQmZmJtLQ0pKWlVfgj/v3799i4cSMeP36M27dvY8KECdi/f7/ANWtHjx6N58+fY9q0aXj8+DHWr1+Pffv2YdKkSQLHysvLY2M+evQI48ePR1ZWFrp3715he9u2bQtzc3N4eHjAysqKTViFOX78OAIDA9nnZseOHeDz+UJXXbewsEDPnj0xcuRIXLp0CXfu3MHgwYOhr6//wwuo/ajg4GBs27YN9+/fx/Pnz7Fr1y7IycmxHRPGxsa4ePEiXr9+LdBJIoyTkxNOnDiBEydO4PHjxxgzZozAtWyNjY0xdOhQeHl54ciRI+z3UnFHl5GRETgcDo4fP453795VeVV4Jycn7Ny5EzExMbh37x6GDh1aboIqjL+/PyIjI3H//n14enpCQ0ODvebylClTEBkZifnz5yMxMREhISFYu3atwGyJtWvXsmtAlNceRUVFTJ06FZMmTUJISAiePXuGuLg4/PfffwgJCQFQNEq8fft2hIaGwsXFBT4+Phg6dCgyMjJE8jyR/5GXk0bj+vpoXL+oI8xYXx2N6+vDQIemzP5qIp9+QGtjFbQyVIa2ojT+stWBjAQXV198AgB4NNdFD5v/zXTrbKkBKy15qPOkYKAsC087PajxpHA5+VPNNIAQQlALk+CpU6dCQkICNjY20NTURH5+Pk6ePInr16+jSZMmGD16NIYPH47Zs2ez+6xcuRIODg7o1q0bnJ2d0bp1a1hbWwuMggUFBcHDwwNTpkyBpaUl3NzcBEaPHR0dMXr0aAwYMACamppYtmxZperL4/Fw8eJFGBoaonfv3rC2tsbw4cORm5v73ZHhlJSUKl+GpTQOh4OTJ0+ibdu2GDZsGOrXr4+//voLL168YKdt9unTB506dUKHDh2gqamJPXv2oEmTJli5ciWWLl2Khg0bIjQ09KfPYeNyuQgLC8OtW7fQsGFDTJo06bsjepW1bds2ZGRkoFmzZhgyZAi8vb0FRk+PHj1a7lTotm3bomnTpuytefPmv1TdheHxeDh06BA6duwIS0tLDB8+HI0bN0Z0dDQ74pSfn4+EhAS2wyYuLg7Xrl3DvXv3YG5uDl1dXfb28uXLCuOFhITAzs4OrVu3xoMHDxAVFSVwnr2JiQlOnDiBs2fPokmTJggICMDWrVvZBcKKRUREsDFbtWqFGzduYP/+/Wjfvn2F8TkcDry8vJCRkSGwirAwKioqOHToEJycnGBtbY2NGzdiz5497DnGpQUFBaF58+bo1q0bHBwcwDAMTp48+UOX0foZKioq2LJlC1q3bo3GjRvj3LlzOHbsGLtytr+/P5KTk2FmZiZwKoUwXl5eGDp0KDw8PNCuXTuYmpqyo8DFNmzYgL59+2Ls2LGwsrLCyJEjkZ1dtOKrvr4+5s2bhxkzZkBbWxvjxo2rUltmzpyJdu3aoVu3bujatSvc3NwEzqn+niVLlmDChAlo3rw50tLScOzYMXbUtVmzZti3bx/CwsLQsGFDzJ07F/7+/gIr6b9//17gvN7y2jN//nzMmTMHixcvhrW1NTp16oQTJ07AxMQE7969w/Dhw+Hn54dmzZoBAObNmwdtbW12DYSffZ7I/zSzMcK1vTNxbW9Rx++yqX1wbe9MzBnTtYZrRkqLe/0Fh++no5u1JmZ2MEE9ZVmsu5zCLpalKicFZdn/TTTkSXPh3lQXc5xNMdbRALJSEgiITkbal2/lhSCE1IC6tjo0h6nMKjq/mezsbOjr6yMgIIC9zij5fRUUFEBbWxunTp0SSNwIIYSIj1zT6u0U+Bq/tlrj1QX/HH70/Y1EbF0v62qPSQgB3nwSTceUnkrtuARarTsn+EfEx8fj8ePHaNmyJT5//sxe2qS6pzuSmvHx40dMmjQJLVq0qOmqEEIIIYQQQmpYnUiCgaJr4yYkJEBaWhrNmzdHTEwMNDQ0arpapBpoaWkJTI8nhBBCCCGE/E9tmsosCnUiCW7atClu3bpV09UghBBCCCGEkF8Op46tDl0nkmBCCCGEEEIIIeWoWzlw7VsdmhBCCCGEEEII+VE0EkwIIYQQQgghdVgdGwimJJgQQgghhBBC6jJaGIsQQgghYpVbUNM1EL/Ji7yrNV5deE6rm5uVZrXHpNeRVJUsZTPkB9DbhhBCCCGEEELqMFodmhBCCCGEEEJI3VG3cmBaHZoQQgghhBBCSN1BI8GEEEIIIYQQUofVsYFgSoIJIYQQQgghpC6j1aEJIYQQQgghhNQZdW1hLDonuARjY2OsXr26pqvxy/Dz84OtrS1739PTE25ubjVWn59Rm+te17Rv3x4TJ04U+bakekVFRYHD4eDTp0/VHpvD4eDIkSMi35YQQgghv4caTYJr6gdscHAwVFRUqj1udSiduP7uTExMcO7cuRr9wS0KPXr0gKGhIWRlZaGrq4shQ4bgzZs35W7/8eNHjB8/HpaWlpCTk4OhoSG8vb3x+fPn78bat28fbG1twePxYGRkhOXLl5fZJioqCs2aNYOMjAzMzc0RHBws8Linpyc4HA57U1dXR6dOnXD37l2hMb99+wYNDQ0sWbJE6OPz58+HtrY28vPzcejQIcyfP/+77QBQpW1/hqg7Uaqzw+17n43g4GCB11LYLTk5uVrqKiqpqano3LmzyLetaWG7Q9HZxQktmjaC+1/9cK+cz9uvFu9pzAmcnDcch6b2RuTKKfj4IrFS+72Mu4gDE7vj8tYFZR7LTHuJ2C3zcWTGABye1heRAZOQWsF3Znlq63Na3TGjTx7E3JF9MLFfByz3GYnkxIflbht7JhyrZo6Bj3sn+Lh3wn9zJ5TZPvPTR+xcswD/DuuBSf2dsG7eZKS/eVnlegG19zmleDUfkwjicERzqy1oJFjMvn37VtNV+G3dvXsXGRkZaNeuXU1X5ad16NAB+/btQ0JCAg4ePIhnz56hb9++5W7/5s0bvHnzBitWrMD9+/cRHByMiIgIDB8+vMI4p06dgru7O0aPHo379+9j/fr1WLVqFdauXctuk5SUhK5du6JDhw64ffs2Jk6ciBEjRuD06dMCx+rUqRNSU1ORmpqKyMhISEpKolu3bkLjSktLY/DgwQgKCirzGMMwCA4OhoeHB6SkpKCmpgZFRcUK21GsKtvWNoWFheDz+WKPM2DAAPZ1TE1NhYODA0aOHClQZmBgUKVj5ufni6m2laOjowMZGRmRb1uTIk6dxIplizFq7D8I238YlpZWGDNqOD58+PBLx3sZF4O7R7bCptNAOE9dDRV9E8RsnIvcL58q3C/7w1vcPbodGqYNyjyW9T4VUYHToahdD+3GLYLLtP9g7foXpKv4OtbW57S6Y966dA6Ht/+Hzn95YfrK7dA3Nse6eZPx5VOG0O2f3I9D8z9cMGF+IKYs3QQVDS2s85uETx/eASj6zt+8eAbev32DUf8uxYxVQVDT1MF/vhOQl/u12ttXVb/7+6YuPKeEADWYBHt6eiI6Ohpr1qwRGG2Ijo5Gy5YtISMjA11dXcyYMQMFBQXsfl++fIG7uzvk5eWhq6uLVatWlRlRzsvLw9SpU6Gvrw95eXm0atUKUVFRAIpGRYYNG4bPnz+zcf38/Nh9c3Jy4OXlBUVFRRgaGmLz5s0C9X758iX69+8PFRUVqKmpoWfPngKjJMUjRgsXLoSenh4sLS2Ftr94xHb79u0wNDSEgoICxo4di8LCQixbtgw6OjrQ0tLCwoULBfb79OkTRowYAU1NTSgpKcHJyQl37twBUDSiM2/ePNy5c4dtW/EI3sqVK9GoUSPIy8vDwMAAY8eORVZWVhVfNUERERFo06YNVFRUoK6ujm7duuHZs2fs48nJyeBwONi3bx/++OMPyMnJoUWLFkhMTMSNGzdgZ2cHBQUFdO7cGe/evWP3u3HjBlxcXKChoQFlZWW0a9cOcXFxZeIfPXoUnTp1gpSUVK2re2mTJk2Cvb09jIyM4OjoiBkzZuDq1avlJhMNGzbEwYMH0b17d5iZmcHJyQkLFy7EsWPHBD4vpe3cuRNubm4YPXo0TE1N0bVrV8ycORNLly4FwzAAgI0bN8LExAQBAQGwtrbGuHHj0LdvX6xatUrgWDIyMtDR0YGOjg5sbW0xY8YMvHz5UuD5KGn48OFITEzEpUuXBMqjo6Px/PlzNoEv/Xlev349LCwsICsrC21tbYHOgdLbZmRkwMPDA6qqquDxeOjcuTOePHnCPl48C+T06dOwtraGgoICm8xXRfv27eHt7Y1p06ZBTU0NOjo6At8jDMPAz88PhoaGkJGRgZ6eHry9vdl9X7x4gUmTJrGf05J1Cw8Ph42NDWRkZJCSkiJ0xoybmxs8PT3Z+3l5eZg+fToMDAzY0ftt27YhOTkZHTp0AACoqqqCw+EI7AcAcnJy7Ouoo6MDaWlp8Hg89r69vT3+++8/gX1sbW0F2svhcLBhwwb06NED8vLyAt9bsbGxaNy4MWRlZWFvb4/79+8LHOvgwYNo0KABZGRkYGxsjICAgHKfdz6fj3r16mHDhg0C5fHx8eByuXjx4gVbn+Ipzt++fcO4ceOgq6sLWVlZGBkZYfHixQJ1Lzkd+t69e3BycoKcnBzU1dXx999/C3xXFn/Hr1ixArq6ulBXV8c///wj9sR/Z0gQevftD7defWBmbo7ZvvMgKyuLI4cO/tLxEqOOwMTBFcatnKGkY4hm/cZCQloGydfOlrsPwy/E9V0BsOk8CPLq2mUev39iJ3RsmqNxj2FQrWcGBQ1d6DVsBXV19Rpp468aT1Qxzx/dC8c/u8OhY1foGpjgrzE+kJaRwZXI40K395zsh7ZdeqOeaX3o1DOC+z8zwDB8JNy9CQBIf/MSyQkP8NfoqTCysIa2vhEGjJ6K/G95uBVT/vtCXO2rqt/9fVMXnlNCgBpMgtesWVNmxEFKSgpdunRBixYtcOfOHWzYsAHbtm3DggX/mwo1efJkxMbGIjw8HGfPnkVMTEyZJGPcuHG4cuUKwsLCcPfuXfTr1w+dOnXCkydP4OjoiNWrV0NJSYmNO3XqVHbfgIAA2NnZIT4+HmPHjsWYMWOQkJAAoGh0w9XVFYqKioiJiUFsbCz7I7rkiG9kZCQSEhJw9uxZHD9e9Eeiffv2ZX58Pnv2DKdOnUJERAT27NmDbdu2oWvXrnj16hWio6OxdOlSzJ49G9euXWP36devH9LT03Hq1CncunULzZo1Q8eOHfHx40cMGDAAU6ZMQYMGDdi2DRgwAADA5XIRGBiIBw8eICQkBOfPn8e0adN+6jXMzs7G5MmTcfPmTURGRoLL5aJXr15lRq98fX0xe/ZsxMXFQVJSEoMGDcK0adOwZs0axMTE4OnTp5g7dy67/ZcvXzB06FBcunQJV69ehYWFBbp06YIvX74IHDc8PBw9e/aslXWvyMePHxEaGgpHR8cqJfifP3+GkpISJCXLX+8uLy8PsrKyAmVycnJ49eoVm0BcuXIFzs7OAtu4urriypUr5R43KysLu3btgrm5ebk/RBs1aoQWLVpg+/btAuVBQUFwdHSElZVVmX1u3rwJb29v+Pv7IyEhAREREWjbtm259fD09MTNmzcRHh6OK1eugGEYdOnSRSBBycnJwYoVK7Bz505cvHgRKSkpAt8BlRUSEgJ5eXlcu3YNy5Ytg7+/P86eLfoBd/DgQaxatQqbNm3CkydPcOTIETRq1AhA0RTuevXqwd/fn/2clqzb0qVLsXXrVjx48ABaWlqVqouHhwf27NmDwMBAPHr0CJs2bYKCggIMDAxw8GDRj4iEhASkpqZizZo1VW5rZfj5+aFXr164d+8evLy82HIfHx8EBATgxo0b0NTURPfu3dnX49atW+jfvz/++usv3Lt3D35+fpgzZ47A9Hs/Pz8YGxsDKPoeGzhwIHbv3i0QOzQ0FK1bt4aRkVGZegUGBiI8PJydaREaGsoer7Ts7Gy4urpCVVUVN27cwP79+3Hu3DmMGzdOYLsLFy7g2bNnuHDhAkJCQhAcHFzmlAFRyv/2DY8ePoC9gyNbxuVyYW/viLt34n/ZePyCfHx69RRa9ZuwZRwuF9r1bfEhOaHc/R6eDoOMgjJM7P8s8xjD5yPt4U0oaOojZsNcHJs9GJErp+D13fK/n4Sprc9pdccsyM/Hy2cJsGzcQuAYlk3skJRwv4I9/+fbt1wUFhaAp6DEHhMAJKWkBY4pKSmNZw8rPwW2tj6nFK/mYxLh6tp06BpbHVpZWVlgxAEAZs2aBQMDA6xduxYcDgdWVlZ48+YNpk+fjrlz5yI7OxshISHYvXs3OnbsCKDoB7Senh573JSUFAQFBSElJYUtnzp1KiIiIhAUFIRFixZBWVkZHA6HjVtSly5dMHbsWADA9OnTsWrVKly4cAGWlpbYu3cv+Hw+tm7dyo7eBAUFQUVFBVFRUfjzz6I/2PLy8ti6dSukpf/3BW9oaAhdXV2BWHw+H9u3b4eioiJsbGzQoUMHJCQk4OTJk0V/ZCwtsXTpUly4cAGtWrXCpUuXcP36daSnp7PT91asWIEjR47gwIED+Pvvv6GgoABJSckybSs5kmRsbIwFCxZg9OjRWL9+fdVfvP/Xp08fgfvbt2+HpqYmHj58iIYNG7LlU6dOhaurKwBgwoQJGDhwICIjI9G6dWsARSOEJX9AOjk5CRx38+bNUFFRQXR0NDvd9vXr17h79+4Pn8tXk3Uvz/Tp07F27Vrk5OTA3t6e7UCpjPfv32P+/Pn4+++/K9zO1dUVkyZNgqenJzp06ICnT5+yI2+pqakwNjZGWloatLUFR1+0tbWRmZmJr1+/Qk5ODgBw/PhxKCgoAChKHnR1dXH8+HFwueX3rQ0fPhxTp05FYGAgFBQU8OXLFxw4cACBgYFCt09JSYG8vDy6desGRUVFGBkZoWnTpkK3ffLkCcLDwxEbGwtHx6I/pqGhoTAwMMCRI0fQr18/AEWdWRs3boSZmRmAok4zf3//Cp83YRo3bgxfX18AgIWFBdauXYvIyEi4uLggJSUFOjo6cHZ2hpSUFAwNDdGyZUsARVO4JSQkoKioWOZzmp+fj/Xr16NJkyZl4pUnMTER+/btw9mzZ9nOC1NTU/ZxNTU1AICWlpZY10IYNGgQhg0bxt5//vw5gKKOJBcXFwBFHQf16tXD4cOH0b9/f6xcuRIdO3bEnDlzAAD169fHw4cPsXz5crbTUENDg32tAMDd3R0BAQFISUmBoaEh+Hw+wsLCMHv2bKH1SklJgYWFBdq0aQMOhyM0US62e/du5ObmYseOHZCXlwcArF27Ft27d8fSpUvZz4WqqirWrl0LCQkJWFlZoWvXroiMjMTIkSN/8NmrWManDBQWFpbpYFJXV0dS0vNfNl5ediYYPh+yiqoC5TKKKsh8+0roPu+fP0Dy1bNw9hHeWZOX9RkFeV+REHkADboMRqPunkh7fAtXghbjZltr2LVoWam61dbntLpjZn35BD6/EIoqagLlSspqePsqpVLHOBqyAcqqGrBqYgcA0KlnBFVNbYTv3ISBY30gLSOHC8f24tOHdHzOqPz019r6nFK8mo9JhKPVoWvQo0eP4ODgwCaYANC6dWtkZWXh1atXeP78OfLz89kfk0BRMl1yyvG9e/dQWFiI+vXrQ0FBgb1FR0cLTHctT+PGjdn/FyfK6enpAIA7d+7g6dOnUFRUZI+rpqaG3NxcgWM3atRIIAEGgB07dghMwQOKktGS5zNqa2vDxsZGIInQ1tYWiJ+VlQV1dXWBtiUlJX23befOnUPHjh2hr68PRUVFDBkyBB8+fEBOTg4ACBxv9OjR332egKKkY+DAgTA1NYWSkhI7upKSIviHseRzWvwjsnhUrHQbAeDt27cYOXIkLCwsoKysDCUlJWRlZQkcNzw8nJ3O/CNqqu6jR48WeK5L8vHxQXx8PM6cOQMJCQl4eHiwU5QrkpmZia5du8LGxkZgimqDBg3YOMWdBSNHjsS4cePQrVs3SEtLw97eHn/99RcAVJi8ClN8zvDt27dx/fp1uLq6onPnzuyIcufOndn4DRoUndc3cOBAFBYWYt++fQCAvXv3gsvlsjMWSnNxcYGRkRFMTU0xZMgQhIaGsu/Z0h49egRJSUm0atWKLVNXV4elpSUePXrElvF4PIGkSldXl30NY2JiBF6f0NDQcttf8r1R+jj9+vXD169fYWpqipEjR+Lw4cMVTlMvJi0tXea433P79m1ISEhU6dz40NBQgXbGxMRUKaYwdnZ2QssdHBzY/6upqQm8Ho8ePWI7lIq1bt0aT548QWFhIYCiTorIyEj2cVtbW1hbW7OjwdHR0UhPT2c7OUrz9PTE7du3YWlpCW9vb5w5c6bcNjx69AhNmjRhE+Di+vD5fHZGEFD02ZKQkGDvl3zthcnLy0NmZqbALS8vr9zt66r83Bxc37USzQaMg4yCstBtGKZoto5ew1ao394NKvVMYeXcD7o2LbB/b1h1VpdUwpmDO3Hr0jmMnLkYUtJFnfcSkpIYOX0R0t+kYNrgzpg8oCMS78XBppl9lf8OEULIj/rtrhOclZUFCQkJ3Lp1S+BHCoAySYcwpaefcjgcdopsVlYWmjdvLvSHsaamJvv/kj+gqhrre/F1dXXZ85tLqigZTE5ORrdu3TBmzBgsXLgQampquHTpEoYPH45v376Bx+Ph9u3b7PZKSkqVqn/37t1hZGSELVu2QE9PD3w+Hw0bNiyzGFjJNhV3cJQuKzkNeejQofjw4QPWrFkDIyMjyMjIwMHBQeC44eHh6NGjR6Xq+SvV3d/fv9yptxoaGtDQ0ED9+vVhbW0NAwMDXL16VSCJKO3Lly/o1KkTFBUVcfjwYYG6nTx5kp12Wjx6y+FwsHTpUixatAhpaWnQ1NRkE4zi0UMdHR28fftWIM7bt2+hpKTEHgcoep+bm5uz97du3QplZWVs2bIFCxYswNatW/H161eB50xJSQl9+/ZFUFAQvLy8EBQUhP79+5f72VRUVERcXByioqJw5swZzJ07F35+frhx48YPd4AI+4wVdzbY2dkJfBZKj4h/7zjF7wUDAwMkJCTg3LlzOHv2LMaOHYvly5cjOjq6winucnJyAp2AQFHnROnOkJLTu0u+JpXVo0cPgc4CfX39crf9Xvxilf3eEwV3d3fs3r0bM2bMwO7du9GpU6dyp+E3a9YMSUlJOHXqFM6dO4f+/fvD2dkZBw4c+OH4Fb32wixevBjz5s0TKJs1xxez5/pVKp6qiiokJCTKLBLz4cMHaGhoVK7SVSCqeDLySuBwucj9IriAUt6XT5BVUi2zffb7NOR8TMflrf9b8b34vXdwck+4/rsRPBUNcLgSUNIxFNhXUdsAaamVHzWqrc9pdcdUUFQBlyuBL58+CpRnfv4IJVW1cvYqcu7Ibpw9uAvj/FdD39hc4DFDcyvMXB2Cr9lZKCjIh6KyKpb7jIShednTYspTW59TilfzMYlwtWkqsyjUaJebtLQ029sPANbW1ux5fMViY2OhqKiIevXqwdTUFFJSUrhx4wb7+OfPn5GY+L/LLTRt2hSFhYVIT0+Hubm5wK146mHpuJXVrFkzPHnyBFpaWmWOrawsvNdalJo1a4a0tDRISkqWiV/8RSGsbbdu3QKfz0dAQADs7e1Rv379MpffKXmsypyH+OHDByQkJGD27Nno2LEjrK2tkZEhfKXIqoqNjYW3tze6dOnCLpjz/v179vGsrCxcuHDhh88Hrsm6l37vlKf4B3VFo0WZmZn4888/IS0tjfDw8DLn+hoZGbFxSic5EhIS0NfXh7S0NPbs2QMHBwe2I8fBwUFg5A0Azp49W2EyDhQlAlwul0189fX12fglp6AOHz4cly5dwvHjx3H58uXvrmgtKSkJZ2dnLFu2DHfv3kVycjLOnz9fZjtra2sUFBQInENf/Frb2NhUGKOYnJycwOvzMytPy8nJoXv37ggMDERUVBSuXLmCe/fuAajad5CmpqbAecOFhYUCi0s1atQIfD4f0dHRQvcvnpVSMp6ioqJAOytKpEvHz8zMRFJSUqXqDgBXr15l/5+RkYHExERYW1sDKHrNYmNjBbaPjY1F/fr1y3RiljRo0CDcv38ft27dwoEDB+Du7l5hHZSUlDBgwABs2bIFe/fuxcGDB/Hx48cy21lbW+POnTvIzs4WqE/x6Sk/aubMmfj8+bPAzWf6zErvLyUtDWubBrh29X/nvfL5fFy7dgWNmwg/PeBniCoeV1IKKvXMkf7kf+d5Mnw+0hPvQN247POpqF0PLtPXwtknkL3pNWgJTfNGcPYJBE9FA1xJKagaWuBLuuB06qx3r6GrV35njrja+KvGE1VMSSkpGJhZsotaFR8j8e4tmFg2LHe/s4dCEbEvGGN9A2Bkbl3udnLyClBUVkX6m5dIefYYjVu2qVS9gNr7nFK8mo9JhOOI6FZb1OhIsLGxMa5du4bk5GR2deTVq1dj/PjxGDduHBISEuDr64vJkyeDy+VCUVERQ4cOhY+PD9TU1KClpQVfX19wuVx29KR+/fpwd3eHh4cHAgIC0LRpU7x79w6RkZFo3LgxunbtCmNjY2RlZSEyMhJNmjQBj8cDj8f7bn3d3d2xfPly9OzZE/7+/qhXrx5evHiBQ4cOYdq0aahXr165+3p4eEBfX7/MlOiqcHZ2hoODA9zc3LBs2TI2mT1x4gR69eoFOzs7GBsbIykpCbdv30a9evXYH7v5+fn477//0L17d8TGxmLjxo0/XA+g6Jw4dXV1bN68Gbq6ukhJScGMGTN+6pjFLCwssHPnTtjZ2SEzMxM+Pj4CP9IjIiJQv359oYvb3Lt3TyBx4XA4Zc6vrMm6C3Pt2jXcuHEDbdq0gaqqKp49e4Y5c+bAzMyMTTxfv36Njh07YseOHWjZsiWbAOfk5GDXrl3sFEugKGkpL4F4//49Dhw4gPbt2yM3NxdBQUHYv3+/QAI1evRorF27FtOmTYOXlxfOnz+Pffv24cSJEwLHysvLQ1paGoCi5Gbt2rXIyspC9+7dK2xv27ZtYW5uDg8PD1hZWbHn7wpz/PhxPH/+HG3btoWqqipOnjwJPp8vNCGxsLBAz549MXLkSGzatAmKioqYMWMG9PX1f7jD5EcFBwejsLAQrVq1Ao/Hw65duyAnJ8d2BhgbG+PixYv466+/ICMjU2Fvt5OTEyZPnowTJ07AzMwMK1euFLjmr7GxMYYOHQovLy8EBgaiSZMmePHiBdLT09G/f38YGRmBw+Hg+PHj6NKlC+Tk5Co1K6Zk/ODgYHTv3h0qKiqYO3duhQlqaf7+/lBXV4e2tjZmzZoFDQ0N9prLU6ZMQYsWLTB//nwMGDAAV65cwdq1awXWKli7di0OHz4s0DFjbGwMR0dHDB8+HIWFhRXOClm5ciV0dXXRtGlTcLlc7N+/Hzo6OkJnEri7u8PX1xdDhw6Fn58f3r17h/Hjx2PIkCEVzgr4HhkZmTKXYcr9/ux4AUOGDsOcf6ejQYOGaNioMXbtDMHXr1/h1qv3D9erOuLVb++GG7tXQdXAHGqG9fEk+igKvuXCuFXR+evXd62EnLI6GnUfCgkpaSjrCp6zLSVXNMOgZLmlU29cDVkGDbOG0DJvhLTHcUh9cB0LfXbWSBt/1XiiiunUcwB2rlkIQ3MrGFvY4MKxfcjLzYV9x64AgB2r50NZXQM9h4wBAJw9tAsndm/F0Mm+UNfSReb/n+crIysHGbmi31pxseehoKQCNU1tvHnxHAe2rkbjln/Aumkr4ZUQY/uq6nd/39SF55QQoIaT4KlTp2Lo0KGwsbHB169fkZSUhJMnT8LHxwdNmjSBmpoahg8fLrDgycqVKzF69Gh069YNSkpKmDZtGl6+fCkwChYUFIQFCxZgypQpeP36NTQ0NGBvb88uTOTo6IjRo0djwIAB+PDhA3x9fQXOpSwPj8fDxYsXMX36dPTu3RtfvnyBvr4+Onbs+N0pxCkpKT99rguHw8HJkycxa9YsDBs2DO/evYOOjg7atm3L/kDr06cPDh06hA4dOuDTp08ICgqCp6cnVq5ciaVLl2LmzJlo27YtFi9eDA8Pjx+uC5fLRVhYGLy9vdGwYUNYWloiMDAQ7du3/6k2AsC2bdvw999/o1mzZjAwMMCiRYsEphAfPXq03B+9pVcOlpCQKHMuZk3WXRgej4dDhw7B19eXXWCqU6dOmD17NvvDOT8/HwkJCez5sHFxceyIZ+kR5aSkpHJXvwWKFieaOnUqGIaBg4MDoqKiBM6zNzExwYkTJzBp0iSsWbMG9erVw9atW9kFwopFRESwi70pKirCysoK+/fv/+7zyOFw4OXlhX///RczZ1Y8GqaiooJDhw7Bz88Pubm5sLCwwJ49e9hzjEsLCgrChAkT0K1bN3z79g1t27bFyZMnf+gyWj9DRUUFS5YsweTJk1FYWIhGjRrh2LFj7JRdf39/jBo1CmZmZsjLy6vw3G8vLy/cuXMHHh4ekJSUxKRJk9jLHhXbsGED/v33X4wdOxYfPnyAoaEh/v33XwBFI/Lz5s3DjBkzMGzYMHh4eFRpJeOZM2ciKSkJ3bp1g7KyMubPn1+lkeAlS5ZgwoQJePLkCWxtbXHs2DF2dLpZs2bYt28f5s6di/nz50NXVxf+/v4CK+m/f/9e6JoH7u7uGDt2LDw8PCrsaFJUVMSyZcvw5MkTSEhIoEWLFuzig6XxeDycPn0aEyZMQIsWLcDj8dCnTx+sXLmy0u0Vl06duyDj40esXxuI9+/fwdLKGus3bYW6mKYLiiqeQbM/kJf9GQ9PhSI3MwPK+qZoM2oeu1hWTsa7MqcAfI9+Ywc06zcWCef24/ahzVDU1IfDsJlo1lz4eenlqa3PaXXHbN7GGVmfP+HEnq34kvER+iYW+Mc3AEr/v1jWx3dvBV7DmFOHUVCQj23LBBer6zzAC10HFs38ycz4gEPb/8OXzx+hpKqOVu07oVP/Yaiq2vqcUryaj0mEqE3DuCLAYSqz8s4vLDs7G/r6+ggICPjutEpS+xUUFEBbWxunTp0SSNwIIaQ2qepIcG00/2zi9zcSoTku9as1Xl0Q8+T99zcSsT8sKPEhVSP7261wVDOy8kSTEirI1I5suta9beLj4/H48WO0bNkSnz9/Zi9tUt3THUnN+PjxIyZNmoQWLVp8f2NCCCGEEELId9W1hbFqXRIMFF0bNyEhAdLS0mjevDliYmJoBbk6QktLq9zrgRJCCCGEEELI99S6JLhp06a4detWTVeDEEIIIYQQQn4LdWwguPYlwYQQQgghhBBCRKiOZcE1ep1gQgghhBBCCCF117p162BsbAxZWVm0atUK169fr3D7/fv3w8rKCrKysmjUqBFOnjxZ5ZiUBBNCCCGEEEJIHcYR0b+q2rt3LyZPngxfX1/ExcWhSZMmcHV1RXp6utDtL1++jIEDB2L48OGIj4+Hm5sb3NzccP/+/aq1t7ZfIokQQgipbegSSaJHl0gSPbpEEqkN6BJJoiGqv0tVfT1atWqFFi1aYO3atQAAPp8PAwMDjB8/HjNmzCiz/YABA5CdnY3jx4+zZfb29rC1tcXGjRsrHZdGggkhhBBCCCGE/LS8vDxkZmYK3PLy8oRu++3bN9y6dQvOzs5sGZfLhbOzM65cuSJ0nytXrghsDwCurq7lbl8uhhBCCCG/vNzcXMbX15fJzc39bWP+7vFqIia1sfbHq4mYv3u8mohZE22sCb6+vgwAgZuvr6/QbV+/fs0AYC5fvixQ7uPjw7Rs2VLoPlJSUszu3bsFytatW8doaWlVqZ40HZoQQgipBTIzM6GsrIzPnz9DSUnpt4z5u8eriZjUxtofryZi/u7xaiJmTbSxJuTl5ZUZ+ZWRkYGMjEyZbd+8eQN9fX1cvnwZDg4ObPm0adMQHR2Na9euldlHWloaISEhGDhwIFu2fv16zJs3D2/fvq10PWkWPSGEEEIIIYSQn1ZewiuMhoYGJCQkyiSvb9++hY6OjtB9dHR0qrR9eeicYEIIIYQQQggh1UpaWhrNmzdHZGQkW8bn8xEZGSkwMlySg4ODwPYAcPbs2XK3Lw+NBBNCCCGEEEIIqXaTJ0/G0KFDYWdnh5YtW2L16tXIzs7GsGHDAAAeHh7Q19fH4sWLAQATJkxAu3btEBAQgK5duyIsLAw3b97E5s2bqxSXkmBCCCGkFpCRkYGvr2+lp5nVxpi/e7yaiEltrP3xaiLm7x6vJmLWRBtrgwEDBuDdu3eYO3cu0tLSYGtri4iICGhrawMAUlJSwOX+b/Kyo6Mjdu/ejdmzZ+Pff/+FhYUFjhw5goYNG1YpLi2MRQghhBBCCCGkzqBzggkhhBBCCCGE1BmUBBNCCCGEEEIIqTMoCSaEEEIIIYQQUmdQEkwIIYQQQgghpM6gJJgQQgghNebixYsoKCgoU15QUICLFy/WQI0IIYT87igJJoQQQn5RTk5O+PTpU5nyzMxMODk5VX+FxKBDhw74+PFjmfLPnz+jQ4cOYon56dMnnDlzBrt27cKOHTsEboQIIyEhgfT09DLlHz58gISEhNjifvv2Da9evUJKSorATRxMTU3x4cOHMuWfPn2CqampWGJWJy8vL3z58qVMeXZ2Nry8vGqgRqQm0SWSCCGEkF8Ul8tFWloatLS0BMrT09Ohr6+P/Px8kcfMzs7GkiVLEBkZifT0dPD5fIHHnz9/LtJ4XC4Xb9++haampkB5YmIi7OzskJmZKdJ4x44dg7u7O7KysqCkpAQOh8M+xuFwhCbkP8vJyQmHDh2CioqKQHlmZibc3Nxw/vx5kcesbl5eXlizZg0UFRUFyrOzszF+/Hhs3769hmomGuV9Ft+8eQMzMzN8/fpVpPGePHkCLy8vXL58WaCcYRhwOBwUFhaKNB5Qfhvfvn0LQ0ND5OXliTTe27dvMXXqVPa7pnRKIuo2SkhIIDU1tUz73r9/Dx0dHaEzUn5GYWEhgoODy/0u/R0+97WZZE1XgBBCCCGC7t69y/7/4cOHSEtLY+8XFhYiIiIC+vr6Yok9YsQIREdHY8iQIdDV1RVIEkWpd+/eAIoST09PT8jIyLCPFRYW4u7du3B0dBR53ClTpsDLywuLFi0Cj8cT+fGFiYqKwrdv38qU5+bmIiYmRuTxqrsjAwBCQkKwZMmSMknw169fsWPHDpEnwdWVYAQGBgIoep9u3boVCgoKAnW4ePEirKysRBKrJE9PT0hKSuL48eNi/RwCQHh4OPv/06dPQ1lZmb1fWFiIyMhIGBsbizyup6cnUlJSMGfOHLG2MTMzEwzDgGEYfPnyBbKysuxjhYWFOHnyZJnEWBQmTJiA4OBgdO3aFQ0bNhTra0iqjpJgQggh5Bdja2sLDocDDocjdNqznJwc/vvvP7HEPnXqFE6cOIHWrVuL5fjFin9oMwwDRUVFyMnJsY9JS0vD3t4eI0eOFHnc169fw9vbu1oS4JrqzKiujgzg908wVq1aBaDofbpx40aBqc/S0tIwNjbGxo0bRR739u3buHXrllgS7NLc3NwAFCX6Q4cOFXhMSkoKxsbGCAgIEHncS5cuISYmBra2tiI/dkkqKirs92n9+vXLPM7hcDBv3jyRxw0LC8O+ffvQpUsXkR+b/DxKggkhhJBfTFJSEhiGgampKa5fvy4wVVhaWhpaWlpiOw9RVVUVampqYjl2SUFBQQAAY2NjTJ06FfLy8mKPCQCurq64efNmtZzjWFOdGdXVkQH8/glGUlISgKJz1w8dOgRVVVWxxitmY2OD9+/fV0us4lF0ExMT3LhxAxoaGtUS18DAoMwUaHG4cOECGIaBk5MTDh48KPD9Ji0tDSMjI+jp6Yk8rrS0NMzNzUV+XCIadE4wIYQQUgsVnxsoart27cLRo0cREhJSLaOljx8/Lne06/Tp03B1dRVpvG3btsHf3x/Dhg1Do0aNICUlJfB4jx49RBbrxYsXNdKZYWJigpMnT8La2lrkxy4tOjq6RhIMPT09REVFCU28xSE3N1dglLuk1NRU6OrqijTe+fPnMXv2bCxatEjo+1RJSUmk8YCKv1NycnJE/n1w5swZBAQEYNOmTWKZbl3aixcvYGBgAC63etYFDggIwPPnz7F27VqaCv0LoiSYEEII+UV5enpi3bp1ZUZJk5OTMWTIELGcT9q0aVM8e/YMDMPA2Ni4zI/vuLg4kcbj8XhYvnw5/vnnH7YsLy8PU6ZMwdatW5GbmyvSeBX9ABbXgkMVEUdnRnV3ZAC/f4JhY2OD3bt3l5m6e/DgQYwePRrv3r0Tabzi57F028S5MFbHjh2xY8eOMlP0r127hiFDhiAxMVGk8VRVVZGTk4OCggLweLwy3zWiXqTOz88Pc+fOLfMe/fz5M0aPHo09e/aINF6vXr1w4cIFqKmpoUGDBmXad+jQIZHGI1VD06EJIYSQX9SdO3fQuHFj7Nq1Cw4ODgCKFiDy9vYW2yWSis8PrC7BwcEYM2YMTpw4gaCgIKSmpmLQoEHg8/liSfJLL6BUHaq7MyMgIADPnj2DtrZ2tXRkAEXT2+fOnVumXFwJxqVLl3DhwgWcOnWqWhKM9u3bw97eHvPmzcP06dORnZ2Nf/75B/v27cPChQtFGgsomsJb3WRlZdG4cWOsX78eAwYMAJ/Ph7+/PxYtWoSxY8eKPN7q1atFfsyKbNu2jb00WvHpEFFRUfDw8ICOjo7I46moqKBXr14iPy4RDRoJJoQQQn5R+fn5+PfffxEYGIgpU6bg6dOnOHXqFFauXCmWRaNqyqtXrzBs2DDEx8cjOzsbnp6eCAgIqLZRTHFr2rQpMjMzy+3MOHz4sEjjfe8cXF9fX5HGA4rO7zQwMCg3wbh+/bpI4w0bNqzCx4vPORelEydOYMSIETA3N0dqaioUFBSwa9cuNGzYUOSxasq6deswbdo09OzZE8nJyXjx4gWCgoLw559/1nTVflpGRgZGjRqFiIgIBAQEIDExEWvWrIGPjw/mzZsHSUkaG6xL6NUmhBBCflFSUlJYvnw5eDwe5s+fD0lJSURHR7OJlDjdunULjx49AgA0aNAATZs2FWu8b9++obCwEIWFhdDV1S33/EtRiI6OxooVK9j22djYwMfHB3/88YdY4l2/fh3//vsv2rdvXy2dGeJIcr/n7t27GDVqFGxtbYUmGKImjiT3ezp37ozevXtjw4YNkJSUxLFjx8SaAH/69Anbtm0T+Bx6eXkJXMJI1P755x+8evUKS5cuhaSkJKKiosRyqbJihYWFOHLkiEAbe/ToIZZz5VVVVbFv3z78+++/GDVqFCQlJXHq1Cl07NhR5LFKevfuHRISEgAAlpaWZa6JTmoIQwghhJBf0rdv35jJkyczMjIyzL///su0bduW0dHRYU6cOCG2mG/fvmU6dOjAcDgcRlVVlVFVVWU4HA7j5OTEpKenizzenj17GBUVFaZ79+5Meno6c+bMGUZfX59xdHRknj17JvJ4O3fuZCQlJZn+/fsza9asYdasWcP079+fkZKSYkJDQ0Uer6S5c+cyHA6HkZKSYi5fvizWWAzDMDdv3mR27tzJ7Ny5k4mLixN7PIZhmJkzZ7JtPHfunNjjpaenMzExMUxMTIxY3p/Fnj59yrRs2ZIxNDRkzpw5w8yaNYuRlpZmfHx8mG/fvok83o0bNxg1NTVGX1+f6dWrF9OrVy+mXr16jLq6OnPr1i2Rx2MYhvn48SPTu3dvRllZmdm8eTPj7u7OyMvLM+vWrRNLvCdPnjAWFhYMj8djmjZtyjRt2pTh8XiMpaUl8/TpU7HEDAwMZHg8HjNo0CDG0tKSsbGxYW7fvi2WWFlZWcywYcMYCQkJhsPhMBwOh5GUlGS8vLyY7OxsscQklUdJMCGEEPKLaty4MWNubs5cuXKFYRiG4fP5zJIlSxgZGRlmzJgxYonZv39/xs7Ojnn48CFb9uDBA8bOzo7566+/RB6Px+Mx69evFyj7+PEj069fP0ZRUVHk8aysrJiVK1eWKQ8ICGCsrKxEHo9hqr8zo7o7Mor9zgmGgoICM2DAACYjI4Mti42NZczMzBhbW1uRx2vTpg3j6enJ5Ofns2X5+fnM0KFDmT/++EPk8RiGYfT09JjWrVszz58/Z8vCwsIYNTU1pkuXLiKP17lzZ6ZTp07Mhw8f2LL3798znTp1Eks8V1dXRl1dndm/fz/DMAyTk5PDjB49mpGVlWWWLl0q8nh///03Y2pqypw8eZL5/Pkz8/nzZ+bEiROMmZkZM3r0aJHHI1VDSTAhhBDyi/Ly8mKysrLKlMfFxTENGjQQS0wlJSXm+vXrZcqvXbvGKCsrizze48ePy31sx44dIo8nLS3NPHnypEz5kydPGBkZGZHHY5jq78yo7o4Mhvn9E4zy3ouZmZmMl5eXyOPJysoyjx49KlP+4MEDRk5OTuTxGIZh/P39mcLCwjLlL1++ZJydnUUej8fjMXfv3i1Tfvv2bUZeXl7k8ZydnZnXr1+XKT9+/Dijo6Mj8njq6urMhQsXypSfP3+e0dDQEHk8UjWUBBNCCCG1UG5urliOq6CgwMTHx5cpj4uLE8vILMMUjXCdPXuW2bhxI5OZmckwDMO8fv2a+fLli8hjmZmZMRs3bixTvmHDBsbc3Fzk8Rim+jszqrsjg2HqToKRl5fHPH78WGCEVhy0tLSY06dPlymPiIhgtLS0xBqbYRjm69evYo+hqqrKxMbGlim/dOkSo6qqKvb4Jb17907kx5STkxPoiCp2//59hsfjiTweqZrquZgbIYQQQn7Izp070bp1a+jp6eHFixcAii4tEhERIZZ4Tk5OmDBhAt68ecOWvX79GpMmTRLLAjIvXrxAo0aN0LNnT/zzzz/s9VaXLl2KqVOnijzelClT4O3tjTFjxmDnzp3YuXMnRo8ejYkTJ4olHlB0aZbSl0cCilaNvnXrlsjj8fn8MpcMAooWWhPXJaLOnj0LPT29MuVdu3bFvXv3RB4vJycH2traZcq1tLSQk5Mj8nhfv37F8OHDwePx0KBBA6SkpAAAxo8fj6VLl4o83oABAzB8+HDs3bsXL1++xMuXLxEWFoYRI0Zg4MCBIo8HFL1v5s+fD319fSgoKOD58+cAgDlz5mDbtm0ij9etWzf8/fffuHbtGpiigTlcvXoVo0ePRo8ePUQeDwBiYmIwePBgODg44PXr1wCKvmMfP34s8lgODg7w9fUVuNb5169fMW/evGpZ3JB8R01n4YQQQggRbv369YyGhgazYMECRk5Ojl0oKigoiGnfvr1YYqakpDC2traMlJQUY2pqypiamjJSUlJM06ZNmZcvX4o8Xs+ePZnBgwczeXl5jIKCAtvGCxcuiG1k9tChQ0zr1q0ZNTU1Rk1NjWndujVz5MgRscQqtmPHDsbR0ZHR1dVlkpOTGYZhmFWrVoklbo8ePZi2bdsKjMy+evWKadeuHePm5ibyeMUuXrzIuLu7M/b29syrV68Yhilqd0xMjMhjOTk5Mf369RMYsczJyWH69evHdOzYUeTxvL29mebNmzMxMTGMvLw8+z49cuSIWM4JzsvLY7y9vRlpaWmGy+UyXC6XkZGRYSZOnCi2WSDz5s1jTE1NmV27dgl834SFhTH29vYij5eRkcH06NGD4XA4jLS0NNtWNzc35tOnTyKPd+DAAUZOTo4ZMWIEIyMjw7bvv//+Yzp37izyePfu3WP09PQYdXV1xsnJiXFycmLU1dUZfX195v79+yKPR6qGkmBCCCHkF2Vtbc0cPnyYYRhGIEG8d+8eo66uLra4fD6fOXPmDBMYGMgEBgYyZ8+eFVssNTU19rzgkm1MSkoS27mP1a26OzOquyODYX7/BMPQ0JA9p7vk+/TJkydiO02AYRgmOzubuXv3LnP37l2xryhsZmbGruhdso2PHj1iVFRUxBY3MTGRCQ8PZ8LDw4Wery8qtra2TEhICMMwgu2Li4tjtLW1xRIzOzub2bx5MzN58mRm8uTJzJYtW5icnByxxCJVQ9cJJoQQQn5RSUlJQq/PKyMjg+zsbLHF5XA4cHFxgYuLi9hiFOPz+SgsLCxT/urVKygqKoo9fnX477//sGXLFri5uWHJkiVsuZ2dnVimYBsYGCAuLg7nzp1jp3laW1vD2dlZ5LGKLViwABs3boSHhwfCwsLY8tatW2PBggUij9ewYUM8efIEoaGhbBsHDhwId3d3yMnJiTzeu3fvoKWlVaY8OzsbHA5H5PGK8Xg8NGrUSGzHL+n169cwNzcvU87n85Gfny+2uBYWFrCwsBDb8YslJCSgbdu2ZcqVlZXx6dMnscTk8XhiuRY4+XmUBBNCCCG/KBMTE9y+fRtGRkYC5REREbC2thZZnMDAQPz999+QlZVFYGBghdt6e3uLLC4A/Pnnn1i9ejU2b94MoCgBz8rKgq+vL7p06SKSGGpqakhMTISGhgZUVVUrTFo+fvwokpgl1URnRnV2ZAC/f4JhZ2eHEydOYPz48QDAvoe2bt0qsvM7e/fujeDgYCgpKaF3794Vbnvo0CGRxCzJxsYGMTExZb5vDhw4IPT9+yMmT56M+fPnQ15eHpMnT65w25UrV4okZjEdHR08ffoUxsbGAuWXLl2CqampSGKEh4ejc+fOkJKSQnh4eIXbiuu8Z1I5lAQTQgghv6jJkyfjn3/+QW5uLhiGwfXr17Fnzx4sXrwYW7duFVmcVatWwd3dHbKysli1alW523E4HJEnwQEBAXB1dYWNjQ1yc3MxaNAgPHnyBBoaGtizZ49IYqxatYodVV61apVYR+6EqY7OjJrsyAB+/wRj0aJF6Ny5Mx4+fIiCggKsWbMGDx8+xOXLlxEdHS2SGMrKyux7U1lZWSTHrIq5c+di6NCheP36Nfh8Pg4dOoSEhATs2LEDx48fF0mM+Ph4dlQ5Pj6+3O3E8RkdOXIkJkyYgO3bt4PD4eDNmze4cuUKpk6dijlz5ogkhpubG9LS0qClpQU3N7dyt+NwOEJnwJDqw2EYhqnpShBCCCFEuNDQUPj5+eHZs2cAAD09PcybNw/Dhw+v4ZqJTkFBAfbu3Ys7d+4gKysLzZo1E9u01pqwdetW+Pn5ISAgAMOHD8fWrVvx7NkztjPjr7/++ukYJiYmuHnzJtTV1WFiYlLudhwOh131V5QWL16MXbt2Yfv27XBxccHJkyfx4sULTJo0CXPmzGFHUH8Gl8tlEwwut/wLnIgrwXj27BmWLFki8D6dPn16tU1Xrg4xMTHw9/cXaOPcuXPx559/1nTVfhrDMFi0aBEWL17MriAuIyODqVOnYv78+TVcO1LdKAkmhBBCaoGcnBxkZWUJPS9RnAoLC3Hv3j0YGRlBVVVV5Me/ePEiHB0dISkpODmtoKAAly9fFjrF9mfExcVBSkqKTVyOHj2KoKAg2NjYwM/PD9LS0iKNV+x378ygBEO0vn79CoZhwOPxABRdSuzw4cOwsbH5LRJSYTIzM3H+/HlYWVnByspKbHG+ffuGp0+fIisrCzY2NlBQUBBbrNI+ffoEFRWVaotHKlBza3IRQgghpCIdOnRgMjIyypR//vyZ6dChg1hiTpgwgdm6dSvDMAxTUFDAODo6MhwOh5GXl2cuXLgg8nhcLpd5+/ZtmfL3798zXC5X5PHs7OyYAwcOMAzDMM+ePWNkZGSYgQMHMubm5syECRNEHq+07Oxsoe0Vp4KCAiY+Pp75+PGj2GPl5eUxDx48YK5du8Z8+fJF7PFKEvZZEZXqfp+6uLgwGzZsYBimqF1aWlpMvXr1GFlZWWb9+vUij8cwDGNiYsK8f/++THlGRgZjYmIi8nj9+vVj/vvvP4Zhii5vZWFhwUhJSTGSkpLsZ1SUhg0bxmRmZpYpz8rKYoYNGybyeEuWLGHCwsLY+3379mU4HA6jp6fH3L59W+TxSNWUP5eEEEIIITUqKioK3759K1Oem5uLmJgYscQ8cOAAmjRpAgA4duwYkpOT8fjxY0yaNAmzZs0SeTyGYYSe//fhwwfIy8uLPF5iYiJsbW0BAPv370e7du2we/duBAcH4+DBgyKPBwBOTk7s4lA8Ho8dzc/MzISTk5PI402cOBHbtm0DUDSS37ZtWzRr1gwGBgaIiooSeTwA8PLywpcvXyAtLQ0bGxu0bNkSCgoKyM7OhpeXl8jjLV26FHv37mXv9+vXD2pqatDX18edO3dEHo8pZ+JkXl6eWGYPxMXF4Y8//gBQ9JnU0dHBixcvsGPHju+e8/2jkpOThU4jz8vLw+vXr0Ue7+LFi2wbDx8+DIZh8OnTJwQGBoplRfGQkBB8/fq1TPnXr1+xY8cOkcfbuHEjDAwMAABnz57FuXPnEBERgc6dO8PHx0fk8UjV0MJYhBBCyC/m7t277P8fPnyItLQ09n5hYSEiIiKgr68vltjv37+Hjo4OAODkyZPo168f6tevDy8vL6xZs0ZkcYpXv+VwOPD09ISMjAz7WGFhIe7evQtHR0eRxSvGMAz4fD4A4Ny5c+jWrRuAossKvX//XuTxgOrvzDhw4AAGDx4MQLAjY+fOnZg1axZiY2NFHjMkJARLliwpc1mr4gRj+/btIo23ceNGhIaGAhBMMPbt2wcfHx+cOXNGJHGKE04Oh4OtW7cKTJ0tLCzExYsXxTJ1Nycnh30uz5w5g969e4PL5cLe3h4vXrwQaaySi4ydPn1aYFGuwsJCREZGllnwTBQ+f/4MNTU1AEWLxPXp0wc8Hg9du3YVaZKYmZkJhmHAMAy+fPkCWVlZ9rHCwkKcPHlSLKeZpKWlsUnw8ePH0b9/f/z5558wNjZGq1atRB6PVA0lwYQQQsgvxtbWFhwOBxwOR+hIoZycHP777z+xxNbW1sbDhw+hq6uLiIgIbNiwAUDRj3IJCQmRxSn+oc0wDBQVFQUWwZKWloa9vb1YLn9jZ2eHBQsWwNnZGdHR0Wz7kpKSoK2tLdJYNdWZUV0dGcDvn2AUr5bOMAw2btwo8BmQlpaGsbExNm7cKLJ4xczNzXHkyBH06tULp0+fxqRJkwAA6enpUFJSEmms4lWMORwOhg4dKvCYlJQUjI2NERAQINKYQFHH05UrV6CmpoaIiAj2+tIZGRkC76OfpaKiwn6f1q9fv8zjHA4H8+bNE1m8Yqqqqnj58iUMDAwQERHBjm4zDEMrQ/8CKAkmhBBCfjFJSUlgGAampqa4fv06NDU12cekpaWhpaUl0oS0pGHDhqF///7Q1dUFh8OBs7MzAODatWsiHfEKCgoCABgbG2Pq1KlimfoszOrVq+Hu7o4jR45g1qxZMDc3B1A0eirqkeea6syoro4M4PdPMJKSkgAAHTp0wKFDh8SyOJwwc+fOxaBBgzBp0iR07NiRvRbxmTNnRHbN3mLFMyNMTExw48YNaGhoiPT45Zk4cSLc3d2hoKAAIyMjtG/fHkDRNGlRrrh94cIFMAwDJycnHDx4kB19Boq+T42MjKCnpyeyeMV69+6NQYMGwcLCAh8+fEDnzp0BFF0aqvh7h9QcWh2aEEIIIQIOHDiAly9fol+/fqhXrx6AoumuKioq6NmzZw3XTjxyc3MhISEBKSkpkR3zxYsXNdKZ4efnh9WrV0NXVxc5OTlITEyEjIwMtm/fji1btuDKlSsiixUdHV0jCca4ceNw/PhxWFhYID4+HsnJyVBQUEBYWBiWLVuGuLg4kcesbmlpaUhNTUWTJk3YS0Jdv34dSkpKYl09uTrdvHkTL1++hIuLCzvV/MSJE1BRUUHr1q1FGuvFixcwMDCo8PJaopSfn481a9bg5cuX8PT0ZDsviq9bPmLEiGqpBxGOkmBCCCGE1Ch/f39oaGhg7NixbNn69evx/v17zJ07twZrVntVd0dGXUgwLl68CB6PBzs7O7bs5s2byMnJEfmlvGpKSkoKpKSkoKury5alpqYiPz8fhoaGNVgzQkSLkmBCCCHkF8blcmFtbY0HDx6wZdbW1khMTBTZtM/AwED8/fffkJWV/e7Ks97e3iKJWZKJiQnMzc1x9uxZtqxjx45ISkrC8+fPf/r4ampqSExMhIaGBlRVVYWuRl3s48ePPx2P/J64XC6srKzw8OFDtkyUn8XevXsjODgYSkpK7MJx5Tl06NBPxxNG3G2cPHky5s+fD3l5eUyePLnCbVeuXPnT8UoT9l3j7OyM58+fi+S7Jjw8HJ07d4aUlJTAgmPC9OjR46fjkR9H5wQTQgghv7Dt27dDRUVFoGzx4sX4/PmzyGKsWrUK7u7ukJWVZRcCEobD4YglCS4+77KkyMhIkR2/eHSw+P8VJcHiIu7OjJruyAB+/wQjKSmpzHT5yMhI5Ofni+T4ysrK7Huz5ArN1enChQvg8XgCZTt27EBOTo5Ijh8fH88+X/Hx8eVuJ67P6NChQwVOSwCAXr16iWxleDc3N6SlpUFLS4tdcEwYDodDi2PVMBoJJoQQQggRs+DgYKioqAj8MD5y5Ag+f/5cZkXeH2FiYoKbN29CXV0dJiYm5W7H4XBEkpAK4+fnB01NTfzzzz9s2bp16/D+/Xv4+vr+9PG5XC6bYFQ07ZoSDELI91ASTAghhBDWpUuX0KZNm2qL9/z5c1y6dAmpqangcrkwNTWFi4uLyC8DU8zZ2RmDBw9G7969xRaDkJ+1YMECuLu7V9ihIS7v3r1DQkICAMDS0rLMyKmo7Nq1C7179y4z8lwditMfcc4KKV69nPyaqmf1AkIIIYRUWqNGjTB//ny8fPmy2mM7OTnBxMQE//77r8DUXVHLzs5Gv379YG5uDk9PT/z7779YuXIlBgwYAH19faxbt04scRs0aICZM2dCR0cH/fr1w9GjR0U2nfVXcenSpWqP6e/vL3TK7NevX+Hv7y/yeNXx2VBTU2OnyaqqqkJNTa3cm6jt378f5ubmcHR0ZBeJE7fs7Gx4eXlBT08Pbdu2Rdu2baGnp4fhw4eLbDp0SZMmTYKWlhYGDRqEkydPVsvo/Y4dO9CoUSPIyclBTk4OjRs3xs6dO8USy9jYGO3atcOWLVuQkZEhlhjkx9FIMCGEEPKL4XK5UFNTw6dPn+Ds7IyRI0eiZ8+ekJQU/1Ie79+/R1hYGPbs2YMrV66gcePGcHd3x8CBA9lVhkVh1KhRePDgATZu3AhZWVnMnDkTpqam8PX1RVhYGMaPH48tW7Zg0KBBIotZjM/n49y5c9i9ezcOHz4MCQkJ9O3bF+7u7mjXrp1IY925cwcBAQFlRrvd3Nzg4+MjltFoaWlp6OvrY+DAgXB3d0eDBg1EHqM0CQkJpKamQktLS6D8w4cP0NLSEnmCIyEhgTZt2mDw4MHo27evWK7fGxISgr/++gsyMjIICQmpcFtRTGkv7cGDBwgNDUVYWBhevXoFFxcXuLu7w83NTSyjp6NGjcK5c+ewdu1a9vJEly5dgre3N1xcXNjrTYtKQUEBIiIisGfPHhw9ehQ8Hg/9+vWDu7u7yK/ZDRQttDVnzhyMGzdOoH3r1q3DggULMGnSJJHGi4+Px+7duxEWFoZ3796hU6dOGDx4MLp37w4ZGRmRxiI/gCGEEELIL4XD4TCvX79mDh8+zHTv3p2RlJRkNDU1mSlTpjAPHz6stno8f/6cWbBgAdOgQQNGQkKC6dChg8iOraGhwdy8eZO9//HjR0ZWVpbJzs5mGIZh1q5dy9ja2oosXnm+fv3K7Nu3j2nSpAnD5XJFeuyIiAhGTk6O6dOnDzN48GCGx+Mx48aNY6ZPn86Ym5szZmZmTGpqqkhjMgzDvHv3jvnvv/8YR0dHhsPhME2aNGGWLVvGvHz5UuSxinE4HCY9Pb1MeWRkJKOhoSHyeHFxcczUqVOZevXqMTIyMkzPnj2Z/fv3M7m5uSKP9Su4dOkSM3bsWEZTU5NRVFQUSwx1dXXmwoULZcrPnz8vltewpOzsbGbXrl1Mly5dGGlpacbU1FTkMYyNjZmQkJAy5cHBwYyxsbHI4xXj8/nM+fPnmREjRjCqqqqMsrIyM2zYMLHFI5VDSTAhhBDyi+FwOMzbt2/Z+2/evGEWLVrEWFhYMFwul3FwcGC2bdtWLXUpKChgjh07xtja2oo0SVRRUWESExPZ+9++fWMkJSXZRCoxMZGRlZUVWTxhUlNTmVWrVjHNmzdnOBwO06pVK5Ee39bWltmwYQN7/8yZM4yVlRXDMEXt7dixI+Pp6SnSmKWJsyODYYpeR1VVVYbL5bL/L74pKSkxXC6XGTt2rEhjllSdCUZhYSGTkJDAxMTEMNHR0QI3cYuPj2emTJnC6Ovri+1zIScnJ7ST7f79+wyPxxNLzJKKO28aNGgg8g4phmEYGRkZ5smTJ2XKExMTGRkZGZHHE+bWrVsi/y4lP4aSYEIIIeQXw+VyBZLgki5cuMAMHjyYkZeXF2sdLl26xIwZM4YdeRo8eDBz6tQpkR3fxcWF+eeff9j7y5cvZ3R1ddn7cXFxYhl9+vz5M7N9+3bG2dmZkZSUZOrXr8/MmzePefr0qchjycrKMklJSex9Pp/PSElJMW/evGEYhmEuXrzIaGpqijxuaeLqyGCYolG0oKAghsPhMGvWrGGCg4PZ2+7du5nLly+LNF5FxJlgXLlyhTExMWG4XC7D4XAEbuJKaIo7MGxsbBgJCQnGycmJ2bp1K/Pp0yexxHNycmL69evHfP36lS3Lyclh+vXrx3Ts2FEsMYtHgDt37sxIS0szZmZmzOzZs5lHjx6JPFaDBg2YhQsXlimfP38+07BhQ5HHK/by5Utm6dKlTJMmTRgJCQmmTZs2Ap1jpGbQdYIJIYSQXwxTwXId7du3R/v27ZGZmSmW2DNnzkRYWBjevHkDFxcXrFmzBj179hT5OYhLliyBi4sLDh48CGlpaaSlpQmcd3n58mV06dJFpDEBQFtbG6qqqhgwYAAWL14MOzs7kccopq+vj4SEBBgbGwMAnj17Bj6fD3V1dQBAvXr1kJWVJbb4sbGxCA0NxYEDB5Cbm4uePXti8eLFIo1RfC6siYkJWrduXS3nrZf06tUr7N69G7t378b9+/fh4OAglkXVRo8eDTs7O5w4cQK6urpiv9a0vb09bty4gcaNG2PYsGEYOHAg9PX1xRpzzZo1cHV1Rb169dCkSRMARee0y8rK4vTp0yKP99dff+H48ePg8Xjo378/5syZAwcHB5HHKTZv3jwMGDAAFy9eZM8Jjo2NRWRkJPbt2yfyeJs2bcLu3bsRGxsLKysruLu74+jRozAyMhJ5LFJ1tDAWIYQQ8osZNmwYAgMDoaioWO2xW7duDXd3d/Tv3x8aGhpijZWamorjx48jLy8PTk5OsLGxEWs8ADh79iw6duxY4XVmRcXf3x9btmzBrFmzICMjg5UrV8LCwgKHDh0CABw+fBizZ88W+SrcpTsy3N3dxdKRUVJcXBykpKTQqFEjAMDRo0cRFBQEGxsb+Pn5QVpaWqTxhCUYgwYNEluCIS8vjzt37sDc3Fwsxy9t1qxZcHd3r5bPREk5OTkIDQ3F48ePAQDW1tZwd3eHnJycyGO5u7vD3d0drq6ukJCQEPnxhbl16xZWrVqFR48eAShq35QpU9C0aVORxzIwMGAXpyvuVCC/DkqCCSGEEELEoKCgALNmzcKuXbuQl5cHV1dXrFmzhu1cuH79OnJzc9G2bVuRxq3OjoxiLVq0wIwZM9CnTx88f/4cNjY26N27N27cuIGuXbti9erVIo1X3QmGk5MTpk2bhk6dOok9Fvk9MAwj9hkD5MdREkwIIYT8ogoKCvDgwQOkpaUBAHR0dGBjYwMpKakarpnovHr1CioqKlBQUBAoz8/Px5UrV0SeIAJFI7QaGhoYO3YsW1Z8Lda5c+eKPF5doKysjLi4OJiZmWHp0qU4f/48Tp8+jdjYWPz1118iv65vdScYxaP2Pj4+aNSoUZnPYOPGjUUe8+LFi+DxeAJT9m/evImcnByxfC5KMzU1xenT/8fencfVmP7/A3+dQ6lU1KixtGm1FSV7kjTaSMNkxhbJx16RoYytEDFGGEumTVnGHhpRJDVUKCqVFrQxRZZIoU7dvz/6dX87TlnGdZ9irufj4fGYrrvH/TonZbru67re7yjo6OhwllFUVAQJCQl06dKFHSspKUFNTQ3U1NSI59XW1iI8PJxdCe7Vq5fY2s9RrQudBFMURVFUK1NXV4fVq1dj165dePHihdC1Dh06YOHChfD29uZsSy+fz0fPnj2Ftun27NkTubm5xPq9lpSUYNy4cUhJSQGPx8PkyZOxe/dudjL86NEjdO3alXh/WaD+/Kq2tjYuXLjAjo0aNQr5+fm4f/8+8TwAePHihdDDjA4dOnCS01Lk5eWRkpICHR0dfPfddxgzZgzc3NxQVFQEPT09vH79uqVf4mdp6meNx+Oxk3Euvk/5fD569OiBrKwsdoz0zyEA7Nixo8lxd3d3LFu2DJ07dwYAuLq6EstsIK73CNT3Xbazs0NpaSn09PQAALm5uVBSUkJERAT69OlDNA9o+t8aCwsL3L9/n7N/a6iPQx97UBRFUVQr4+npiX379sHX1xeWlpb49ttvAdRPDKOjo7Fq1SpUV1dj06ZNnOQHBwejY8eOQmMbN24UmZB/Dk9PT/D5fFy7dg3l5eXw9PTEyJEjER0dDQUFBQDvLxD2OfLz80XGYmJiOMkKDAzE1q1bkZOTIzSup6eHJUuWwNnZmZNccTzIaMzY2Bjr16+HhYUF4uLisGfPHgD1X+uG71/SxDnBaOp7hmv5+fkiK84xMTGoqakhmrNo0SJ069ZNZDW0rq4OYWFhkJCQAI/H42QSHBsbK3JWPSwsDFVVVcSzZs2ahd69eyM5OZn9N+b58+eYMWMGZs+ejYSEBOKZ06dPh5KSktDY999/jydPnhDPoj4NXQmmKIqiqFamc+fOCA0NhaWlZZPXo6Ki4OjoiEePHon5lZHTrVs3hIeHY+DAgQCAt2/fwsHBAcXFxewv+lytBIvLr7/+Ci8vL7i6ujb5MGPHjh3w8vLCzz//TDx737596NixI+zt7dmxU6dO4cWLF2xFZ5LS09MxZcoUFBUVwd3dHWvWrAEAuLi44OnTpzh06BDxTC8vLygpKWHBggXs2K5du/DkyRM2n/qwuXPn4tq1azh06BB69uzJjktISCAtLU3sxbm4Ii0tjeTkZPTu3VtoPCMjAwMGDPjidytQn4ZOgimKoiiqlWnfvj2SkpLYSrvvSk9Px9ChQzltr8M1WVlZ3Lp1S+i8oUAggIODA+7fv48DBw6gX79+xCfB9+/fx5UrV1BSUgI+nw9NTU189913kJeXJ5oDAOrq6vj1118xceLEJq8fOXIES5cuRVFREfHs1uLNmzdo06bNF3mO/cyZM7C2toaEhATOnDnz3s+1s7MT06viRnh4ONzc3LBs2TIsXLgQgPgmwWVlZexOCT09PZGVU1L69u0LPz8/mJubC41funQJbm5uuH37Nie5wP/taqGFsloRcTcmpiiKoijq/WxsbJjRo0czZWVlItfKysoYKysrxtbWlmhmnz59mLVr1zJFRUVE79scfX195vjx4yLjNTU1jL29PaOmpsbw+Xxiea9evWJ++OEHhsfjMTwej+Hz+Uznzp2ZNm3aMLKysszOnTuJZTWQkpJisrKymr2emZnJSEtLE89tKc+fP2cCAgIYT09P5unTpwzDMExKSgrz4MED4lne3t5MZWWlyHhVVRXj7e1NJIPH4zGPHj1i/7u5P6S+TxUUFNif+Y4dOzIKCgrN/uHCgwcPGHNzc8bKyoopKSlh2rZty2RmZnKSxTD1P5NOTk5M27Zt2a9l27ZtmZkzZzb5d/tvvHjxgv1z9uxZpnfv3syxY8eY4uJipri4mDl27Bijr6/PnD17lkjeu0JDQ5k+ffow7dq1Y9q1a8fo6+szYWFhnGRRn4auBFMURVFUK1NcXAwbGxtkZ2dDX19faBvt7du30atXL/z1119QVVUllsnn86GoqIjy8nJYWFjgf//7H6dVUz08PJCamoqoqCiRawKBABMmTMBff/1FbCV4zpw5yMzMhL+/P6SkpLB8+XJoampizZo1OHz4MFxcXBAQEIDJkycTyQMAU1NTdO/eHUFBQSJfx9raWsycORMFBQWIi4sjlpmWlobffvtNZLXb3t4eS5cu5WTFG6jfnTBq1Ch07NgRBQUFyMnJgaamJlauXImioiKEhYURzWvTpg1KSkqgrKwsNP706VMoKyt/kdvoQ0ND8dNPP6Fdu3YIDQ197+dysaUdqF+x9PX1xY4dO1BWVob09HTOVoLnzJmDixcvYufOnRg2bBgA4MqVK3B1dcV3333Hniv/HHw+X2j1lXlnRbbxx6S/Z7Zu3YpVq1Zh4cKFQu9v165dWL9+PRYvXkw0j/o0dBJMURRFUa1QXV0doqKikJSUJFRVeMiQIRg9ejTxytB8Ph8PHjzA9evXERwcjHPnzkFBQQGOjo5wdnYWOitIgkAgQFVVVbOTMoFAgIcPH0JdXZ1InpKSEs6fP4/+/fsDqC+I07VrVzx9+hQyMjLYtWsXAgMDcevWLSJ5QP3E0NLSEjU1NTA1NRV6mBEfHw9JSUlER0cTq0obFRWF77//HjY2NpCWlsbJkycxc+ZMtG/fHidOnADDMLhy5Qpb7ZckCwsLGBkZYfPmzZCTk0NaWho0NTWRkJCAyZMno6CggGgen8/Ho0ePRLbOXrp0CT/++CPKysqI5v3X3Lx5E3///TccHR3ZIlKkderUCcePH4eZmZnQeGxsLCZOnEjk7/BTHjCNGDHis/Ma6969O7y9veHo6Cg0HhoaCi8vrxYptkb9HzoJpiiKoigKfD4fpaWl7MpaSUkJ9u3bh5CQENy7dw+DBg3CrFmzMHPmzBZ+pf+OgoICrl+/zp5BrqmpgYyMDP755x8oKSkhLy8PBgYGxIvjVFRU4MCBA00+zJg8eTLRlVlDQ0PMmTMHc+fOBQBcuHABrq6uuHPnDmpqamBtbQ1VVVWEhIQQy2zQuE9w40lwYWEh9PT08ObNGyI5CgoK4PF4ePHiBeTl5YVW+Wpra/Hq1SvMnTsXu3btIpLX2I0bNxAbG4vHjx+jrq5O6NrWrVuJ59XV1eHu3btN5nHdJ/jq1aswNjZGu3btOMuQkZFBSkqKyAO2zMxMDBw4EJWVlcSyBAIBNmzYgJkzZ0JFRYXYfd9HSkoKGRkZ0NbWFhrPy8uDvr4+sZ8J6t+hk2CKoiiKaoUYhkFBQQFUVVXRtm1bVFdXIzw8HG/fvoWNjQ06depENK+57aUAcPnyZQQFBSE8PJxoMa6SkhLExMRAUVERFhYWkJSUZK9VVlbit99+w+rVq4lkjR49Grq6uti5cycAYMuWLdi6dSv++ecfAMCtW7cwevToL3oFUVpaGnfu3IGGhgaA+u+hdu3aobCwEF26dMHff/+NCRMm4PHjx8SzlZWVERUVBUNDQ6FJ8IULFzBz5kwUFxcTyQkNDQXDMJg5cya2bdsm1G9ZUlISGhoaGDJkCJGsxjZs2ICVK1dCT08P3377rdDkm8fj4dKlS0TzkpKSMHnyZBQWFoq0CuOqL3Fj8vLySE1NhaamJmcZo0aNwjfffIOwsDBISUkBAF6/fo3p06fj2bNnuHjxItE8OTk53L59m/354FqfPn0wefJk/PLLL0Lj69evx5EjRzgtxEV9BPEfQ6YoiqIo6n2ys7MZdXV1hs/nM9ra2sz9+/eZ/v37M+3bt2dkZGSYTp06Mbm5uUQzGxcBas6LFy+I5V2/fp3p2LEjIy8vz0hLSzPa2tpMRkYGe720tJRoYayUlBRGUVGR6dy5M6OmpsZISkoyf/75J3t9586djKOjI7G895kxYwbz8OFD4vfV0tJizp8/z36cl5fHtGnThnn79i3DMAxz//59zgpxOTs7M/b29kx1dTUjKyvL3L9/nyksLGQMDQ0ZNzc34nmXL19mampqiN+3OcrKykxISIjY8vr27cs4ODgwWVlZzPPnz5ny8nKhP1yTlZVl7t27x2nG7du3ma5duzLffPMNY25uzpibmzPffPMN061bN6F/C0ixs7Nj9u3bR/y+zTl+/DjTpk0bxtLSklm7di2zdu1axtLSkmnbti1z8uRJsb0Oqml0JZiiKIqiWhl7e3swDIP169cjODgYUVFR0NXVxbFjx1BXVwcHBwd06NAB+/fvJ5bp5OSEHTt2QE5Ojtg93+e7776DqqoqAgMDUVlZCQ8PDxw9ehQXLlyAoaEhHj16RLxPcElJCf766y+8ffsW5ubmnLd+SU9Pb3Lc2NgYR48eZVfZDAwMiOStXbsWAQEBWLFiBdq1a4etW7dCR0cHJ0+eBFDfBmflypXIzMwkktfYixcv8MMPPyA5ORkVFRXo2rUrSktLMWTIEERGRqJ9+/ZE827evAkJCQm2jdjp06cREhKCXr16wcvLS2hXAQldunRBfHy8UEsvLrVv3x5paWkiW2nFpfFqPpeqqqpw8OBBZGdnAwB69uyJKVOmQFpamniWv78/vL29MWXKFPTv31/ke5KLNlcpKSnw8/PDnTt3ANS/vyVLlsDQ0JB4FvVp6CSYoiiKoloZZWVlREdHo1+/fqisrIScnBzi4+NhYmICAEhISMCkSZNQWFjYwq/031NUVERSUhJ0dXXZMV9fX2zevBlRUVFQU1MjPgkWt4bKtE39qtUwTnJrq0AgwIoVK3DgwAG8ffsWlpaW2L59O7t1/vr163jz5g2n50mvXr2KtLQ0vHr1CkZGRrCwsOAkZ8CAAfD09MSECRNw//599OrVC+PHj8eNGzdga2uLbdu2Ec3bvHkz/vnnH+L3bY65uTmWLVsGKysrseS969ChQxg3bhzxhxct6X3FBMWxxZxqXegkmKIoiqJaGRkZGWRnZ0NNTQ1A/apMamoqtLS0ANS3UNLR0eGksIpAIEBmZqZQEadevXpBQkKCaI6ioiIuX74ssgq6ZcsW+Pj4IDg4GD/88APxX0wfPHiAjh07QlZWVmi8pqYGiYmJRCeI/fr1g4qKCrZs2cKubDEMAx0dHZw7d45dVSRVAbslhYWF4ccffxQppFRdXY3Dhw+LVMj9XI0LcW3atAmXLl1CVFQUrl69ip9++onYGeQGdXV1sLW1RW5ubpM/Dw2r7aQ0rNovXboU+vr6Inmkdg80NnPmTGzfvl1kN0hlZSVcXFwQHBxMPLMxTU1NREVFiW21XRxqa2sRHh7OrgT36tWL09Zz1Mejk2CKoiiKamW0tbWxb98+duV3z549mDp1KvvL6c2bN2Fra4uSkhJimXV1dVi9ejV27dqFFy9eCF3r0KEDFi5cCG9vb2KtmUxNTTF58mS2knFjmzdvxurVq1FTU0NsElxSUoJx48YhJSUFPB4PkydPxu7du9nJMBfbr6urq7Fs2TJcuHABBw4cYLdASkhIIC0tjdPt2C9evBB6kNG4gBQXxN23V15eHikpKdDR0cF3332HMWPGwM3NDUVFRdDT0yNe5XvhwoUIDAzEyJEjRQpjASBecbupnzMudg801tzf4ZMnT9C5c2cIBAIiOTt27Ghy3N3dHcuWLWNbeLm6uhLJaymZmZmws7NDaWkp9PT0AAC5ublQUlJCREQEsdZo1L9DH0NQFEVRVCtjYWGB7OxsdhI8b948oevR0dEwMjIimunp6Yl9+/bB19cXlpaWQj1to6OjsWrVKlRXV2PTpk1E8hwdHREXF9fkJHjZsmVgGAb+/v5EsoD698fn83Ht2jWUl5fD09MTI0eORHR0NNsHlfS6gKSkJLZt24Zz587Bzs4O8+fPh4eHB9GMdwUGBmLr1q3IyckRGtfT08OSJUvg7OzMSW7D5OxdDx484GQCbmxsjPXr18PCwgJxcXHYs2cPACA/P5/93iUpNDQUJ06cgK2tLfF7N0WcPWRfvnwJhmHAMAwqKirYSs1A/UpmZGRkk1Xj/61FixahW7duIquhdXV1CAsLg4SEBHg8HvFJ8Nq1a997nVQl+gazZs1C7969kZyczP4b8/z5c8yYMQOzZ89GQkIC0Tzq09CVYIqiKIr6wuTn50NKSgpdunQhds/OnTsjNDQUlpaWTV6PioqCo6MjHj16RCxTnLp164bw8HAMHDgQAPD27Vs4ODiguLgYMTExqKmp4fQM8qNHj+Dk5IRXr14hMTGRk5XgX3/9FV5eXnB1dW3yQcaOHTvg5eWFn3/+mVimoaEheDwe0tLS0Lt3b6GJTW1tLfLz82FlZYWjR48SywTqi45NmTIFRUVFcHd3x5o1awAALi4uePr0KQ4dOkQ0T11dHVFRUejRowfR+7YGDWfXm8Pj8eDt7Y0VK1YQyZs7dy6uXbuGQ4cOCfUI5nqHxLvFqGpqapCfn4+2bdtCS0sLN2/eJJonLS2N5ORk9O7dW2g8IyMDAwYMIL5bgfo0dCWYoiiKor4w3bt3J37Phoq+zenSpQsqKyuJ5wLi2br74sULdjUGANq1a4eTJ0/CwcEBI0eOxIEDB4hnNvbtt98iMjISO3bswDfffAN5eXniGTt37kRISAgmTpwoNN6zZ0+YmZmhb9++WLp0KdFJsL29PQAgNTUVlpaWQmetG/r2TpgwgVheAwMDgyb7rP76669o06YN8TwvLy+sWbMGISEhkJGRIX5/ADhz5gysra0hISGBM2fOvPdzSVYyjo2NBcMwMDc3x4kTJ6CoqMhek5SUhLq6+nv/bfhU/v7+CA8Ph6WlJZYtW4aFCxcSu/f73Lp1S2Ts5cuXmDFjBr7//nviebq6unj06JHIJPjx48ctVvWb+j90JZiiKIqiWqEnT54gODgYiYmJQhPEoUOHYsaMGVBSUiKaZ2trC4FAgIMHD7LVhBu/lmnTpqFNmzb466+/iGWKc+uugYEB1qxZIzIhEwgEcHBwwM2bN/HgwYMvukKstLQ0bt68KbS61lhWVhaMjY1RVVVFPDs0NBQ//vij0FZarpWXl+P48eO4d+8eli5dCkVFRdy8eRPffvstunXrRjTL0NAQ9+7dA8Mw0NDQEClURWIVkc/no7S0FMrKyi1SybiwsBCqqqrEzv1/yMOHD+Ho6AhJSUmEhIRAVVWV87PyTbl9+zbGjh2LgoKCz77Xy5cv2f++cuUKli1bBi8vLwwePBgAkJSUhLVr18LX1xc2NjafnUf9e3QSTFEURVGtzI0bN2BpaQkZGRlYWFgIbWuNiYlBVVUVoqKiYGxsTCyzuLgYNjY2yM7Ohr6+vlDm7du30atXL/z1119QVVUlkifurbseHh5ITU1FVFSUyDWBQIAJEybgr7/+4mRyIa6K1KampujevTuCgoJEzlvW1tZi5syZKCgoQFxcHJG8lpSeno5Ro0ahY8eOKCgoQE5ODjQ1NbFy5UoUFRUhLCyMaJ63t/d7rzdsx/7SPX/+HEFBQULVjJ2cnIRWh0liGAa+vr7YsWMHysrKkJ6eLvZJ8JUrVzB27Fg8f/78s+/17tbyhmlWw1jjj7/kB25fAzoJpiiKoqhWZvDgwejbty/8/f1FzuoxDIO5c+ciPT0diYmJRHPr6uoQFRWFpKQkodXnIUOGYPTo0URXiNTV1fHrr7+KbN1tcOTIESxduhRFRUVE8gQCAaqqqprdhiwQCPDw4UOi7YrEXZE6PT0dlpaWqKmpgampqdCDhfj4eEhKSiI6OpqTqrS1tbXw8/PD0aNHUVRUhOrqaqHrz549I5pnYWEBIyMjbN68GXJyckhLS4OmpiYSEhIwefJkIqt6/zXx8fEYO3YsOnTowD5gS0lJQXl5OSIiIjjtL33z5k38/fffcHR0FDq2QEJRURFUVFSwc+dOoXGGYVBSUoL9+/djxIgRRM6Rf8oDphEjRnx2HvXv0UkwRVEURbUy0tLSuHXrVrNFeLKzs2FoaPhFF1Zpya274jJ9+nTk5ORg586dbEVqHo/HVqR+9OgRunTpgrq6OmKZFRUVOHDgQJMPMiZPnszJWWSgvrJuYGAglixZgpUrV2LFihUoKCjAqVOnsHr1auKVfhv3CW48CS4sLISenh4nPbQbzJ8/H2vXrhU5NkDajRs3EBsbi8ePH4t8j2zdupV4nr6+PoYMGYI9e/aw56pra2sxf/58JCQkNHkGm5SrV6/C2NhYpM80CQ2tnwYNGiQ0zufzoaSkBHNzcyxfvlykP/LnEAgE2LBhA2bOnAkVFRVi96XIoZNgiqIoimplunfvDm9vbzg6OjZ5PSwsDKtXrya+2sUwDAoKCqCqqoq2bduiuroa4eHhePv2LWxsbIj+0t8SW3dLSkoQExMDRUVFWFhYQFJSkr1WWVmJ3377jWiblJauSC1OWlpa2LFjB2xtbSEnJ4fU1FR2LCkpiXi1ZmVlZURFRcHQ0FBoEnzhwgXMnDkTxcXFRPMak5eXR2pqKjQ1NTnL2LBhA1auXAk9PT2RvsQ8Hg+XLl0iniktLY3U1FS2p22DnJwc9OvXj9OHblx+TRuftRYnOTk53L59GxoaGmLNpT4OrQ5NURRFUa3Mzz//jNmzZyMlJQWjRo0SORMcEBCALVu2EM3MycmBpaUliouLoampiejoaDg4OCA7OxsMw0BGRgYJCQnQ0dEhkrdz505YWlqic+fO7926S8qNGzcwevRo1NXVoaamBt26dcOpU6fYyq2vXr2Ct7c30UlwS1ekbuDk5AQfHx+iFX7fVVpaCn19fQCArKwsXrx4AQAYM2YMVq1aRTzPzs4Oa9euZVsv8Xg8FBUVwcPDg5Nq1I2JY/1o+/btCA4OxowZMzjPamBkZIQ7d+6ITILv3LmDvn37cprN9df0fS2guGJubo64uDg6CW6l6CSYoiiKolqZBQsWoFOnTvDz88Pu3bvZlcI2bdqgf//+2LdvX7Nnaf8tDw8P9O3bFxEREQgODoatrS10dXWRmJiIuro6ODg4YO3atdi/fz+RPAMDA+Tm5rJbd+/fvw+gfuvu+vXriW/d/eWXX/D9998jMDAQlZWV8PDwwIgRI3DhwgWR/qGkaGpqIj09XejBQdu2bXHs2DE4ODhgzJgxRPPS09ObHD948CDGjRvHrrIZGBgQzQUAFRUVlJSUQE1NDVpaWoiOjoaRkRFu3LjByRbX3377DT/88AOUlZXx+vVrjBgxAqWlpRgyZAh8fHyI54kbn8/HsGHDxJrp6uoKNzc33L17V6ia8a5du+Dr6yv0/cXF9xCXVq1a9cHWVqS3mFtbW8PT0xO3b99G//790b59e6HrJNtcUZ+OboemKIqiqFaspqYGT548AQB06tRJpDULKcrKyoiOjka/fv1QWVkJOTk5xMfHw8TEBACQkJCASZMmobCwkJN8rikqKiIpKQm6urrsmK+vLzZv3oyoqCioqakR35os7orUDZVpm/rVrmGcq6q0np6ekJeXxy+//IIjR45g6tSp0NDQQFFRERYvXgxfX1/imUD9WdK0tDS8evUKRkZGsLCw4CRH3DZv3ox//vkH27ZtE1vmhwrfcfk9dOjQIYwbN05kokgCn8/HkCFDhI4/vIuLLeYt0eaK+nh0EkxRFEVRX4jLly9j0KBBkJaWJn5vGRkZZGdnQ01NDQCEznUC9S2UdHR0OC04BHC3dVdRURGXL18WWcHasmULfHx8EBwcjB9++IHoL6birkjdr18/qKioYMuWLez3CMMw0NHRwblz59gVaZIVsJuTmJiIxMRE6OjoYOzYscTvHxYWhh9//FFklbm6uhqHDx9u9jz95yorK2P7Wuvp6RHv192grq4Otra2yM3NRa9evUQefp08eZJ45qc84CLxPTRz5kxs375dpCBVZWUlXFxcEBwc/NkZQMudCaZaNzoJpiiKoqgvhKSkJNLS0pqtqPw5tLW1sW/fPnbld8+ePZg6dSr7C+rNmzdha2uLkpISInnNbd01NjbG0aNHiW/dNTU1xeTJkzF37lyRa5s3b8bq1atRU1PD+eoMl1Vwq6ursWzZMly4cAEHDhxgt3lLSEggLS1N7P1XudRQ8ffdic3Tp0+hrKxM/O+xYWK2f/9+oeMJjo6O+P333z+41fZTLVy4EIGBgRg5cqRIYSwACAkJIZrXEpr7O3zy5Ak6d+4MgUDAaQ7130bPBFMURVFUK2NkZNTkeMMWWikpKQD1E1NSLCwskJ2dzU6C582bJ3S94YwnKf369Wt26+6ECROIb7t0dHREXFxck5PgZcuWgWEY+Pv7E8l6H2tra86q4EpKSmLbtm04d+4c7OzsMH/+fHh4eBDPaXDmzJmP/lzS5x8bvj/e9eDBA3To0IFoFgC4u7sjLi4OZ86cYc/qXrlyBa6urliyZAn27NlDNC80NBQnTpyAra0t0fu+T3N/nzweD1JSUtDW1kb37t0/O+fly5dgGAYMw6CiooL99wyorwwfGRlJdMLa1L8xXD6MarB27dr3XidZhI/6dHQlmKIoiqJaGQkJCVhYWLDFaYD6X+TWrVuHuXPnsr8grlmzRmyvKT8/H1JSUujSpQuR+7Wmrbvi1LidD5cePXoEJycnvHr1ComJiZysBH/oDGkDkg8zDA0NwePxkJaWht69ewu116qtrUV+fj6srKzYqtGkdOrUCcePH4eZmZnQeGxsLCZOnIiysjKieerq6oiKimq2VzgXmjtT3vgssImJCU6dOiVU9fzf5jSHx+PB29sbK1as+NcZjYWGhuKnn34SmvCKo83VuwX3ampqkJ+fj7Zt20JLS4voQ0zq09GVYIqiKIpqZS5fvozp06dj4MCBWLNmDTvZ8PHxwYIFC1pkWyuJFaDGrl+/jmXLlmHChAlCW3cBoGvXrpxOfl+8eIHS0lIA9dWouVg5bGnffvstIiMjsWPHDnzzzTdEK203qKurI37PD7G3twcApKamwtLSErKysuw1SUlJaGhocNIiqaqqim3j1ZiysjKqqqqI53l5eWHNmjUICQkhvtW6ORcuXMCKFSvg4+PD9ra+fv06Vq1ahZUrV6JDhw6YM2cOfv75ZwQFBf3rnNjYWDAMA3Nzc5w4cQKKiorsNUlJSairqxOtCTB9+nSRMXGsAd66dUtk7OXLl5gxYwa+//57zvOp96MrwRRFURTVCr148QJz585Ffn4+Dh48CC0tLc7Pdj558gTBwcFITEwUmiQOHToUM2bM4KQI0Llz5zB79mx26267du04e4+BgYHYunUrW9iogZ6eHpYsWQJnZ2fime/isgpua/HgwQN07dr1o1eK/43Q0FD8+OOPQltpuTRq1Ch88803CAsLYzNfv36N6dOn49mzZ7h48SLRPENDQ9y7dw8Mw0BDQ0OkMBYXq4h9+vTBH3/8gaFDhwqNX716FbNnz0ZmZiYuXryImTNnoqio6LPzCgsLoaqqyun3SXPEtSOjKbdv38bYsWNRUFAg9mzq/9CVYIqiKIpqhTp06IA///wTISEhMDExgbe393u3EH6uGzduwNLSEjIyMrCwsGBbCT169Ag7duyAr68voqKiYGxsTDTX2toaycnJcHJywrlz54jeu7Fff/0VXl5ecHV1haWlJbuq9+jRI0RHR8PNzQ3Pnz/Hzz//zNlrAIDhw4fj2bNnnE6CHzx4gI4dOwqtkgL12zETExNhamrKWTYA9OrVi/Otpk2t7nFp+/btsLS0hIqKCvr27QsASEtLg5SUVJMtsD5Xw4q3ON27d6/JHQPy8vJsH28dHR22ZdvnUldXx/PnzxEUFIQ7d+4AqP/ecXJyElod5sLevXubXNkXhxcvXuDFixctkk39H7oSTFEURVGtXF5eHqZMmYLk5GRkZGRwsko6ePBg9O3bF/7+/iKTbYZhMHfuXKSnpyMxMZF4doMdO3YgNjYWv//+O1RUVIjeW11dHb/++ismTpzY5PUjR45g6dKlRFa43iUQCODt7Y0dO3bg1atXAABZWVm4uLhgzZo1xHo/l5SUYNy4cUhJSQGPx8PkyZOxe/dudjL86NEj4r2QmyKOVbba2lr4+fnh6NGjKCoqQnV1tdD1Z8+eEc+sqqrCwYMHkZ2dDQDo2bMnpkyZwknLspZgYmICOTk5hIWFsbs+ysrK4OjoiMrKSsTHx+PixYtYsGCByG6KfyM+Ph5jx45Fhw4d2IdrKSkpKC8vR0REBPGHNeJqyVRUVAQVFRXs3LlTaJxhGJSUlGD//v0YMWIEDh06RCSP+nfoJJiiKIqivgB1dXWoqKiAvLw8JyvC0tLSuHXrVrOFeLKzs2FoaIjXr18TzxYHaWlp3Lx5s9n2UllZWTA2NubkfOe8efNw8uRJrF27FkOGDAFQ30fXy8sL9vb2xCoLT58+HTk5Odi5cyfKy8vh6ekJHo+H6OhoKCgo4NGjR+jSpQvnZ3nFMQlevXo1AgMDsWTJEqxcuRIrVqxAQUEBTp06hdWrV8PV1ZWzbHGbP38+1q5di06dOnGak5OTg3HjxiE/Px+qqqoA6vuDa2pq4vTp09DV1cWpU6dQUVGBadOmfXaevr4+hgwZgj179qBNmzYA6h9uzJ8/HwkJCbh9+/ZnZzQm7pZMgwYNEhrn8/lQUlKCubk5li9fLjIZp8SLToIpiqIoikL37t3h7e0NR0fHJq+HhYVh9erVxM+xiWvrrqmpKbp3746goCChisJA/S/eM2fOREFBAeLi4ojkNdahQwccPnwY1tbWQuORkZGYNGkSsa2R3bp1Q3h4OFvU6O3bt3BwcEBxcTFiYmJQU1MjlpXgjRs3Yt68eejYsSNnGVpaWtixYwdsbW0hJyeH1NRUdiwpKYnzVTZNTU1ERUWxVcy5JI5Kxg3q6uoQHR2N3NxcAPXn5b/77jtOzu1KS0sjNTUVenp6QuM5OTno168fsQduDS2ZFBQUkJeXJ1TboLa2FhEREfD09MQ///xDJI/P56O0tJT2JW7l6JlgiqIoimqFdu/ejZMnT0JRURFz5szBqFGj2GtPnjzBwIED2XN6JPz888+YPXs2UlJSMGrUKKEzszExMQgICMCWLVuI5X1o6+6zZ88wcuRIYhO2nTt3wtLSEp07d4apqanQ+4uPj4ekpCSio6OJZL2rXbt20NDQEBnv3r07JCUlieW8ePFCqHVNu3btcPLkSTg4OGDkyJE4cOAAsazmVFdXY/z48SIPNUgrLS2Fvr4+gPqt5Q0PEsaMGYNVq1YRy9mxY0eT40VFRQgJCUHnzp0BgNOVZ3GuV/H5fFhZWcHKyorzLCMjI9y5c0dkEnznzh323DUJHTt2BI/HA4/HY2sdNNbQkokkLus3UGTQSTBFURRFtTI7duzA8uXL4eTkhBcvXsDGxgZeXl5Yvnw5gPrVi8LCQqKZCxYsQKdOneDn54fdu3ezk882bdqgf//+2LdvX7Pnaf8NT09P8Pl8XLt2jd26O3LkSHbrLkD2l38DAwPk5ubiwIEDSEpKYh8gdO7cGevXr8fkyZM5aSMEAAsXLsS6desQEhLC9ip9+/YtfHx8sHDhQmI5mpqaSE9PF1qdbNu2LY4dOwYHBweMGTOGWNa7qqqq4OLigtDQUABAbm4uNDU14eLigm7dusHT05NonoqKCkpKSqCmpgYtLS1ER0fDyMgIN27cEOoH+7kWLVqEbt26ieweqKurQ1hYGCQkJMDj8b6a7ddFRUWQkJAQ6gdeUlKCmpoaqKmpEc1ydXWFm5sb7t69y/ZET0pKwq5du+Dr64v09HT2cw0MDP51jrhbMgHAqlWrPtjaauvWrUQzqU9Dt0NTFEVRVCvTu3dvrFixApMnTwYAJCQkwN7eHnPnzsXatWs5L3BUU1PDVoDt1KkTscJNjbWWrbtcGT9+vNDHFy9eRLt27YQqC1dXV2PUqFE4efIkkUwPDw+kpqY2Wa1YIBBgwoQJ+Ouvvzj5mrq5ueHq1avYtm0brKyskJ6ezp4l9fLyarJn6ufw9PSEvLw8fvnlFxw5cgRTp06FhoYGioqKsHjxYvj6+hLJmTt3Lq5du4ZDhw4JnSfnul1ZS+Hz+ejRoweysrLYsZ49eyI3N5f4982HtljzeDwwDAMej0cku7CwEGpqapyv0vL5fAwZMuS9uzx4PB4uXbrE6eug3o9OgimKoiiqlZGRkUFWVpbQFtqMjAxYWFjAyckJixYtEtsE8fLlyxg0aBDxCriysrK4deuW0KqlQCCAg4MD7t+/jwMHDqBfv36cv0cnJyf4+PgQXwlycnL66M8NCQkhkikQCFBVVdXsirZAIMDDhw+hrq5OJK8xdXV1HDlyBIMHDxYqjHX37l0YGRnh5cuXxDMbS0xMRGJiInR0dDB27Fii9w4PD4ebmxuWLVvGrtyLaxJcVlbGVmLW09PjpFd3Y3FxcZCRkcGAAQPYsRs3bqCqqgojRowgmvUpu1lIfM+GhIRAVlYWDg4OQuPHjh1DVVUVsbZb9Ezwl4FOgimKoiiqlVFTU8PBgwcxfPhwofGsrCyYm5vD0tISBw4cEMskWFJSEmlpac1WVf63DAwMsGbNGkyYMEFovGEifPPmTTx48IDYe2y8tbIxY2NjHD16lC069DnbLlujq1evwtjYmOgW4abIyMggIyMDmpqaQpPgtLQ0mJqafvF9UR8+fAhHR0dISkoiJCQEqqqqnE6CG9r27N+/X+hogqOjI37//fcPbrWlROnq6mLv3r0YOXKk0HhcXBxmz55NpO0T0HwVaqp1oWeCKYqiKKqVMTExwcmTJ0Umwb169UJMTIzIL3EkGBkZNTnesI1WSkoKAHDz5k0iedbW1vjjjz9EJsENZ1gnTJiABw8eEMkCgH79+rHbK981YcIEotsuWxNra2uxVBY2NjbG2bNn4eLiAuD/CgMFBgaybaE+15kzZz76c+3s7IhkNujWrRsuXrwIX19fGBoacl6syt3dHXFxcThz5gyGDRsGALhy5QpcXV2xZMkSYm21WlJzf588Hg9SUlLQ1tZG9+7dieUVFRU1eT91dXWi/cGb+t4Q18Mo6uPRSTBFURRFtTKenp5ISUlp8lrv3r1x6dIlnDhxgmjm7du3YWFhwRaoAep/mUtLS8PIkSOJr2r4+Pg025O3bdu2OHHiBB4+fEgsz8DAACoqKtiyZQu7tZthGOjo6ODcuXOctbqJjIxkq3w7OTkJrag/f/4cEyZM4PRsoLg2/G3YsAHW1tbIysqCQCDA9u3bkZWVhYSEBGJtp+zt7T/q87h6mMHj8bB8+XJYWlri77//FioeRdqJEydw/PhxmJmZsWM2NjaQlpbGxIkTiU2CjYyMEBMTAwUFBRgaGr73vCypB2AN7O3tm3ww1fgssImJCU6dOiVU9fzfUlZWRnp6ukil9rS0NHzzzTefff8GISEh6NChg9CYuB5GUR+PToIpiqIoqpUxMDB477bcPn36oE+fPkQzL1++jOnTp2PgwIFYs2YNW7TGx8cHCxYsIL7ts23btu+txty2bVuiZ1evX7+OZcuWYcKECThw4AAMDQ3Za127duXknOyhQ4fg6OgIKysr5OTk4Pfff0dgYCCmTJkCoL6dEBd9iVuCiYkJUlNT4evrC319fbZac2JiItvK6HPV1dURuc/nev36NebOncvpql5VVRXbxqsxZWXlZh8e/Rvjxo1j38fHPmQg5cKFC1ixYgV8fHzYAnnXr1/HqlWrsHLlSnTo0AFz5szBzz//jKCgoM/OmzRpElxdXSEnJ8f2H4+Li4Obmxt++umnz75/g6bOFtPTp60PPRNMURRFUa3U9evXkZiYiNLSUgD17XyGDBnC/sJI2osXLzB37lzk5+fj4MGD0NLS4rQAUFZWFnbu3Nnke1y4cCEnmefOncPs2bMxf/58eHh4oF27dpy9P0NDQzg5ObHtc44ePYqZM2di+/btcHZ25rzKN1A/ER83bhzat2/PWUZLe/DgAbp27frBasOkyMvLc76qN2rUKHzzzTcICwtjjyK8fv0a06dPx7Nnz3Dx4kXOssWlT58++OOPPzB06FCh8atXr2L27NnIzMzExYsXMXPmTCLblaurqzFt2jQcO3aMbXlVV1cHR0dH+Pv7E+3Z/a7G5+Sp1oGuBFMURVFUK/P48WOMHz8eCQkJUFNTY1eEHj16hMWLF2PYsGE4ceIE8S3KHTp0wJ9//omQkBCYmJjA29ubs3Yi586dg729PYyMjDBu3Dih93jhwgUYGRnh9OnTsLS0JJprbW2N5ORkODk54dy5c0Tv/a68vDyhSsUTJ06EkpIS7OzsUFNTg++//57TfAAYPnw4nj17xvkkuLnqzzweD+3ateN0gtGrVy+xbjUVx/rR9u3bYWlpCRUVFaG2WlJSUk22wPoS3bt3r8ndIPLy8mwfbx0dHbZd2+eSlJTEkSNHsG7dOqSlpUFaWhr6+vqc7AJ51969e5tc2adaEENRFEVRVKsyYcIEZsiQIUx2drbItezsbGbo0KHMDz/8wOlryM3NZQYMGMDweDwmMzOT+P0NDAyYVatWNXt9zZo1jL6+PvHcxrZv387Y29szxcXFnNy/S5cuTGJiosj45cuXGVlZWWbFihUMn88nnltTU8OsXLmSkZeXZ/h8PsPn8xl5eXlmxYoVTHV1NfE8hmEYHo/HZjX1R01NjVm9ejVTW1tLPFtWVpa5d+8e8fu2dF5lZSXzxx9/MO7u7oy7uzsTEBDAVFVVcZYnEAiYX3/9lRkwYADz7bffMgoKCkJ/SBs2bBhjZWXFPH78mB17/PgxY2VlxQwfPpxhGIa5cOECo6urSzxbHJycnJiXL1+KjL969YpxcnJqgVdENUa3Q1MURVFUKyMnJ4f4+Hihc6uNpaSkwMzMDBUVFZy+jrq6OlRUVEBeXp74irC0tDRSU1Ohp6fX5PWcnBz069cPr1+/JporTvb29ujbty+8vb1Frl2+fBljxozB69eviW+HnjdvHk6ePIm1a9eylZkTExPh5eUFe3t7TioLh4WFYcWKFZgxY4bQ+c7Q0FCsXLkSZWVl2LJlC5YuXYpffvmFaLa4t5p+rVvMV69ejcDAQCxZsgQrV67EihUrUFBQgFOnTmH16tXstn5ScnJyMG7cOOTn50NVVRUAUFxcDE1NTZw+fRq6uro4deoUKioqMG3atM/Omzlz5nuvBwcHf3ZGY821Snry5Ak6d+4MgUBANI/6NHQ7NEVRFEW1Mu3atWt2eykAVFRUiKXVBp/PF6lySoqGhgbOnj3b7CT47NmznGxTfPDgATp27AhZWVmh8ZqaGiQmJrIFc0hYvHgxEhISmrxmZmaGiIgIhIWFEctrcOjQIRw+fBjW1tbsmIGBAVRVVTFp0iROJsGhoaH47bffMHHiRHZs7Nix0NfXx969exETEwM1NTX4+PgQnwT/8ssvUFRUJHrPdzWc5ZaTk8PkyZPZ8YZ+vqQnUO/S1NREVFQUZ1XMAeDgwYMICAiAra0tvLy8MGnSJGhpacHAwABJSUnEJ8F6enrIyspCdHQ0cnNz2bHvvvuOPd9NsljX8+fPhT6uqalBRkYGysvLYW5uTizn5cuXYBgGDMOgoqKCPdMNALW1tYiMjKQ9hFsBuhJMURRFUa3MggULcPbsWfj5+WHUqFHsubmXL18iJiYG7u7uGDNmDH7//Xeiubt372bb+cyZMwejRo1irz158gQDBw5kz+p9rmPHjmHy5MmwtraGhYWF0JngmJgYnD9/HocOHRLpI/xvlZSUYNy4cUhJSQGPx8PkyZOxe/dudjIsjiJV4qKsrIy4uDihdkwAcOfOHZiamqKsrIx4prS0NNLT00UmaXl5eejbty+qqqqQn5+P3r17E61uXF1djfz8fGhpabHFjrggrlW9HTt2NDnu7u6OZcuWoXPnzgBAfEIKAO3bt8edO3egpqaGLl264OzZszAyMsL9+/dhaGiIFy9eEM9saXV1dZg3bx60tLSwbNkyIvfk8/nv3TnD4/Hg7e2NFStWEMmj/h26EkxRFEVRrczWrVtRV1eHn376CQKBgC0qVF1djbZt28LZ2Rlbtmwhmrljxw4sX74cTk5OePHiBWxsbODl5YXly5cDqF/BKCwsJJbn4OCAbt26YceOHfjtt99EqkNfvnyZ3cpLgqenJ/h8Pq5du4by8nJ4enpi5MiRiI6OZnuQintdQCAQ4J9//oGamhrR+y5cuBDr1q1DSEgIu2Pg7du38PHxwcKFC4lmNVBVVUVQUBB8fX2FxoOCgtitrk+fPiXS7xWobyHk4uKC0NBQAEBubi40NTXh4uKCbt26wdPTk0iOuFf1Fi1ahG7duolM6Ovq6hAWFgYJCQnweDxOJsEqKiooKSmBmpoatLS02DZXN27c4GznSVFRESQkJIR6LpeUlKCmpob4z0VT+Hw+3N3dYWZmRmwSHBsbC4ZhYG5ujhMnTgjtUpCUlIS6ujq6du1KJIv69+gkmKIoiqJamXbt2mHPnj3YtGkTUlJShCaI/fv3f29/3X9r7969CAgIYLd6zps3D/b29nj9+jXWrl1LPA8Ahg4dKtIehSsXL15EeHg4jI2NAdS3YXFwcIC5uTliYmIAgLNK2M3JzMyEkZERkdXn8ePHC3188eJFkcrC1dXVQqv7JG3ZsgUODg44d+4cBgwYAABITk5GdnY2jh8/DgC4ceMGfvzxRyJ5y5cvR1paGi5fvgwrKyt23MLCAl5eXsQmwR07dgSPxwOPx4Ourq7I9YZVPVJmz56Na9eu4dChQ0Ir+RISEoiOjuaklVeD77//HjExMRg0aBBcXFwwdepUBAUFoaioCIsXL+YkU0NDAz169EBWVhY7Zm5ujtzcXLHtyrh37x7R87kjRowAAOTn50NNTU3s/65QH4dOgimKoiiqlZKXl8fIkSPFkpWfny80IR06dCguXboECwsL1NTUYNGiRWJ5HVx58eKF0Cpku3btcPLkSTg4OGDkyJE4cOBAC766z/fu2e13t5E3rMZyxc7ODjk5Odi7dy9ycnIA1LejOnXqFDQ0NADUP1gh5dSpUzhy5AgGDx4sNMno3bs37t27RyxH3Kt6/v7+CA8Ph6WlJZYtW8bZyn1TGq/i//jjj1BTU0NiYiJ0dHSEWn2RFBsbCxkZGaGxsLAwolvmG7i7uwt9zDAMSkpKcPbsWUyfPp143qVLlyArKwsHBweh8WPHjqGqqoqTTOrj0TPBFEVRFNUKvX79GikpKVBUVBRZ/Xnz5g2OHj0KR0dHYnlqamo4ePAghg8fLjSelZUFc3NzWFpa4sCBA0RXZyIjI9kzyE5OTkIrX8+fP8eECRNw6dIlIlkGBgZYs2aNyORQIBDAwcEBN2/exIMHD4i+PyMjo/def/36tVhXvLhSU1MDKysr+Pv7c1q4qTEZGRlkZGRAU1NTqDp0WloaTE1NiZ9fLSwshKqqKluwiWsPHz6Eo6MjJCUlERISAlVVVaSlpXG6Evy1e/eBIp/Ph5KSEszNzTFz5kziZ8p1dXWxd+9ekdy4uDjMnj2bfVhEtQw6CaYoiqKoViY3NxejR49GUVEReDweTExM8Oeff7IrTlwUcZo8eTK+/fZb+Pn5iVzLzMzEyJEj8fTpU2KZhw4dgqOjI6ysrPDixQskJycjMDAQU6ZMAUD+PXp4eCA1NRVRUVEi1wQCASZMmIC//vqL6NdUSkoKP/30E7p3797k9ZKSEgQEBHzxk2AAUFJSQkJCgtgmwaampnBwcICLiwvk5OSQnp6O7t27w8XFBXl5eTh//jzxzOfPnyMoKAh37twBAPTq1QtOTk6cVaZmGAa+vr7YsWMHysrKkJ6ezvkkOC8vD7GxsXj8+DHq6uqErq1evZrTbC4xDIPi4mIoKSlBWlpaLJlSUlLIzs5md0I0KCgoQM+ePb/o9m9fAzoJpiiKoqhW5vvvv0dNTQ327duH8vJyLFq0CFlZWbh8+TLU1NQ4mQSnp6cjJSUFTk5OTV7PyMjAiRMnsGbNGiJ5hoaGcHJyYgv8HD16lG1D4+zsTPw9CgQCVFVVNXueWiAQ4OHDh0TbMhkbG8PZ2bnZbcCpqano37//F7u63tjixYvRrl07kcJYXLly5Qqsra0xdepU7Nu3D3PmzEFWVhYSEhIQFxeH/v37E82Lj4/H2LFj0aFDB/ZceUpKCsrLyxEREUG0tda7bt68ib///huOjo7ECos1JSAgAPPmzUOnTp3QuXNnoW3mPB4PN2/e/OwMIyMjxMTEQEFBAYaGhu89L0sir0FdXR2kpKSQmZkptgc1ampq2LlzJ+zs7ITGT58+jQULFuDBgwdieR1U0+iZYIqiKIpqZRISEnDx4kV06tQJnTp1QkREBObPn4/hw4cjNjYW7du3J55pYGAAAwODZq/36dMHffr0IZaXl5cndM5w4sSJUFJSgp2dHWpqavD9998TywKAtm3bvregWNu2bYn3JR42bNh7tzzKyckRnTw1Xl3PycnB77//LrS6Xl1djbi4OGJ5jQkEAgQHB+PixYvo37+/yPfo1q1bieaZmJggNTUVvr6+0NfXZysZJyYmQl9fn2gWUN+27Mcff8SePXvQpk0bAPXVoefPn48FCxbg9u3bxDMbvH79GnPnzuW8N/j69evh4+MDDw8PzjLGjRvHvg+SPYA/hM/nQ0dHB0+fPhXbJHjSpElwdXUV+jmPi4uDm5sbfvrpJ7G8Bqp5dCWYoiiKoloZeXl5XLt2TaTP68KFC3H69GkcOnQIZmZmnGyjvX79OhITE0VaFg0cOJBoTteuXXHy5EkMHjxYaDwuLg5jxoyBm5sbNm7cSPQ9ZmVlYefOnU2+v4ULF37x5y3Fvbre2PsKuPF4PE5Wn8VJWloaqamp0NPTExrPyclBv379ON3aKi8vj9TUVGhqanKWIc6clhIREYHNmzdjz549RB/oNae6uhrTpk3DsWPH2PPGdXV1cHR0hL+/P9v6jmoZdCWYoiiKolqZHj16IDk5WWQSvHPnTgAQ2V5HwuPHjzF+/HgkJCRATU0N3377LYD6s7mLFy/GsGHDcOLECWI9UQcOHIhz586JTIJHjBiBiIgIjBkzhkhOg3PnzsHe3h5GRkYYN26c0Pu7cOECjIyMcPr0aVhaWhLNFSdxr643Fhsby9m9m/Ly5csmx3k8Htq1a0d8gmFkZIQ7d+6ITILv3LnDtqHiirjWqxwcHBAdHY25c+eKJU/cHB0dUVVVhb59+0JSUlLkbPCzZ8+I5klKSuLIkSNYt24d0tLSIC0tDX19feI7Tqh/h06CKYqiKKqV+f777/Hnn39i2rRpItd27tyJuro6+Pv7E82cP38+6urqmvxFPycnBzNnzsSCBQtw7NgxInmLFy9GQkJCk9fMzMwQERGBsLAwIlkA4OnpCQ8PjyZ7Hnt5ecHLywtLly7lfBKsr6+PyMhITloWycvL49GjR0KFuEaOHIm//voLY8aM+arOIDb0722OiooKZsyYgTVr1hCp6Ozq6go3NzfcvXuXfXCTlJSEXbt2wdfXF+np6eznvu9YQWumra2NVatWISkpCfr6+pCQkBC63rDDgJTa2lr4+fnh6NGjKCoqQnV1tdB10pNSPz+/FunZq6ur22SPaapl0e3QFEVRFEVBTk4O8fHxMDQ0bPJ6SkoKzMzMUFFRIeZXRkZz21kbiGNbKwChdj6k2dvbo2/fvvD29ha5dvnyZYwZMwavX7/mrBp1cnJysxOakydPEs0KCwvDihUrMGPGDHar/vXr1xEaGoqVK1eirKwMW7ZswdKlS/HLL798dt6HJtI8Hg8Mw4DH4xH/+h46dAjjxo3jpBZAY81VMQfq39/9+/eJ5q1evRqBgYFYsmQJVq5ciRUrVqCgoACnTp3C6tWriU+6xW3mzJnvvR4cHCymV0I1ha4EUxRFUdQX4M8//4SdnR1nvwi3a9eu2S2mAFBRUcF5YR5bW1sEBgaiS5cuxO+toaGBs2fPNjsJPnv27Be/TVHcq+uNHT58GI6OjrC0tER0dDRGjx6N3NxcPHr0iJNt2KGhofjtt98wceJEdmzs2LHQ19fH3r17ERMTAzU1Nfj4+BCZBOfn53/2PT5Fw1luOTk5TJ48mR2vrKyEi4sLJxMocb/HgwcPIiAgALa2tvDy8sKkSZOgpaUFAwMDJCUlEZ8Et2nTBiUlJSJHOp4+fQplZWXiDy+eP38u9HFNTQ0yMjJQXl4Oc3NzolnUp6MrwRRFURT1BeC6aM2CBQtw9uxZ+Pn5YdSoUWwl5ZcvXyImJgbu7u4YM2YMfv/9d07yAW5XSY8dO4bJkyfD2toaFhYWQmeCY2JicP78eRw6dAgTJkwgnt2YjY0NgoKCOJnotyQDAwPMmTMHCxYsYP8eu3fvjjlz5qBLly5Nrk5/DmlpaaSnp4tU+s3Ly0Pfvn1RVVWF/Px89O7dG1VVVUSzxaG5CduTJ0/QuXNnCAQCzrKrq6uRn58PLS0ttqATF9q3b487d+5ATU0NXbp0wdmzZ2FkZIT79+/D0NAQL168IJrH5/NRWloq8jX9559/oKWlJZa+vXV1dZg3bx60tLSwbNkyzvOo5tGVYIqiKIr6AnD9zHrr1q2oq6vDTz/9BIFAwBYWqq6uRtu2beHs7IwtW7Zw+hq45ODggG7dumHHjh347bffRKpDX758GUOGDOH8dURGRnKe0RyBQIB//vkHampqxO9979492NraAqgvCFRZWQkej4fFixfD3Nyc+CRYVVUVQUFBIn2Jg4KC2PPWT58+JdZX98yZM02O83g8SElJQVtb+73biT/Wy5cvwTAMGIZBRUUFpKSk2Gu1tbWIjIwkVpzuXVVVVXBxcUFoaCgAIDc3F5qamnBxcUG3bt3g6elJNE9FRQUlJSVQU1ODlpYW2+bqxo0bRHed7NixA0D931VgYCBkZWXZa7W1tYiPj0ePHj2I5b0Pn8+Hu7s7zMzM6CS4hdFJMEVRFEVRaNeuHfbs2YNNmzYhJSVFaJLYv3//9/bYJUVdXV2kGA9JQ4cOxdChQzm7f2PNTZqawkW176ZkZmbCyMiIkzPBCgoK7Hnxbt26ISMjA/r6+igvL+dkJXbLli1wcHDAuXPnMGDAAAD1Z5Kzs7Nx/PhxAMCNGzfw448/Esmzt7dnz/021vgssImJCU6dOvVZE++Ggl88Hq/JYko8Ho/4A4UGy5cvR1paGi5fvgwrKyt23MLCAl5eXsQnwd9//z1iYmIwaNAguLi4YOrUqQgKCkJRUREWL15MLMfPzw9A/YNEf39/ts8zUP/ARkNDg3ihwfe5d+8epyv51Meh26EpiqIo6gtw5coVGBsbC60MfQ2KioqgqqoqUrWVYRgUFxdzsmopDh9bkZiLQkrNSUtL42wSPHnyZBgbG8Pd3R3r1q3D77//jnHjxrHtp0gXxgKAgoIC7N27Fzk5OQAAPT09zJkzBxoaGsSzYmJisGLFCvj4+AgV4lq1ahVWrlyJDh06YM6cORg0aBCCgoL+dU5cXBwYhoG5uTlOnDgBRUVF9pqkpCTU1dXRtWvXz34/TVFXV8eRI0cwePBgoaMJd+/ehZGR0XtrBpCQmJiIxMRE6OjoCLX6ImXkyJE4efIksd0BH+Lu7i70McMwKCkpwdmzZzF9+nS25R3VMugkmKIoiqIoAMDr16+RkpICRUVF9OrVS+jamzdvcPToUTg6OhLNFGexmsjISJw8eRKKiopwcnIS6sP8/PlzTJgwAZcuXSKWJ25GRkbvvf769Wvk5uZyMgl+9uwZ3rx5g65du6Kurg6bN29GQkICdHR0sHLlSqITj5qaGlhZWcHf31/kTDBX+vTpgz/++ENkJ8HVq1cxe/ZsZGZm4uLFi5g5cyaKioo+O6+wsBCqqqpE2jt9LBkZGWRkZEBTU1NoEpyWlgZTU1PiZ3RbWm1tLW7fvg11dXVOJsYjR44U+pjP50NJSQnm5uaYOXMmp+etqQ+jX32KoiiKaqW6d+/+3r6WJFuW5ObmYvTo0SgqKmK3dv7555/sqtOLFy/g5OREfBLcsJX0Xa9evSK66n3o0CE4OjrCysoKOTk5+P333xEYGIgpU6YAqD/7HBcXRyyvJWRlZeGnn35q9mxqSUkJcnNzOcluvGLJ5/OJb51tTEJCQqgvrzjcu3evySMB8vLy7M+hjo4Onjx5QiRPXV0dz58/R1BQEO7cuQMA6NWrF5ycnIS+1iQZGxvj7NmzcHFxAQD25zIwMJCz8/J5eXmIjY3F48ePUVdXJ3Rt9erVRLMWLVoEfX19ODs7o7a2FqampkhMTISMjAz++usvmJmZEctiGAahoaFQUlKCtLQ0sftS5NBJMEVRFEW1UosWLRL6uKamBrdu3cL58+exdOlSolkeHh7o06cPkpOTUV5ejkWLFsHExASXL1/mZEtyw1ZBHo+HVatWQUZGhr1WW1uLa9euoV+/fsTyfv31V2zdupVtu3L06FHMnDkTb968gbOzM7Gc5lRWViIuLq7JHrqkWsH06dMHgwYNwrx585q8npqaioCAACJZAD5peyzpM+UN50ffLYzFlf79+2Pp0qUICwuDkpISAKCsrAzLli1jzyTn5eWxRbk+V3x8PMaOHYsOHTrA2NgYQH2Bp7Vr1yIiIgKmpqZEchrbsGEDrK2tkZWVBYFAgO3btyMrKwsJCQmcPCAKCAjAvHnz0KlTJ3Tu3FnoYRiPxyM+CT527BimTp0KAIiIiEBBQQGys7Oxf/9+rFixAlevXiWWxTAMtLW1kZmZKbbdCtSnoZNgiqIoimql3NzcmhzftWsXkpOTiWYlJCTg4sWL6NSpEzp16oSIiAjMnz8fw4cPR2xsLPH+xLdu3QJQ/8vi7du32WrUQP3Zx759++Lnn38mlpeXlyd0znDixIlQUlKCnZ0dampqOOll2+DWrVuwsbFBVVUVKisroaioiCdPnkBGRgbKysrEJsHDhg1jz8c2RU5OjujkqaGI0/s0rPST3oItEAgQHByMixcvon///iLfn1u3biWaFxQUhHHjxkFFRYWd6BYXF0NTUxOnT58GUL97YeXKlUTyFixYgB9//BF79uxhCznV1tZi/vz5WLBgAW7fvk0kpzETExOkpqbC19cX+vr6bLXmxMRE6OvrE89bv349fHx84OHhQfzeTXn69Ck6d+4MoP5ohIODA3R1ddmezCTx+Xzo6Ojg6dOndBLcStEzwRRFURT1hbl//z769etHtFCNvLw8rl27JnROFgAWLlyI06dP49ChQzAzMyM+mXFycsL27ds5rz7dtWtXnDx5EoMHDxYaj4uLw5gxY+Dm5oaNGzdycl7WzMwMurq68Pf3R4cOHZCWlgYJCQlMnToVbm5uGD9+PPFMcfiU1cERI0YQzX73vGVjPB6Pk7PddXV1iI6OZreU6+np4bvvvuPk3K60tDRSU1Ohp6cnNJ6Tk4N+/fqJpact17juff4udXV1BAQEYNSoUejevTv27NkDW1tbZGZmwsTEBM+fPyeaFxERgc2bN2PPnj3o06cP0XtTn4+uBFMURVHUF+b48ePEzwX26NEDycnJIpPghgqmXLXxCQkJAQDcvXsX9+7dg6mpKaSlpZs9K/xvDRw4EOfOnROZBI8YMQIREREYM2YMsax3paamYu/eveDz+WjTpg3evn0LTU1NbN68GdOnT/9iJ8GkJ7afIjY2VuyZfD4fVlZWQu2DuGJkZIQ7d+6ITILv3LmDvn37cpZbW1uL8PBwoXPI48aN46SIk4ODA6KjozF37lzi926Kk5MTJk6ciC5duoDH48HCwgIAcO3aNU76BDs6OqKqqgp9+/aFpKSkyNngZ8+eEc+kPh6dBFMURVFUK2VoaCg0EWQYBqWlpSgrK8Pu3buJZn3//ff4888/MW3aNJFrO3fuRF1dHSe9NJ89ewYHBwfExsaCx+MhLy8PmpqacHZ2hoKCAn777TciOYsXL0ZCQkKT18zMzBAREYGwsDAiWe+SkJBgVwuVlZVRVFSEnj17okOHDiguLuYks4G+vj4iIyOJnVVtrZniUFRUBAkJCXTp0oUdKykpQU1NDfFz866urnBzc8Pdu3fZBzdJSUnYtWsXfH19hQqDGRgYEMnMzMyEnZ0dSktL2cn3pk2boKSkhIiICOKrmdra2li1ahWSkpKgr68v0iOc1DGBBl5eXujTpw+Ki4vh4OCAdu3aAaivUM9FITc/Pz+iD/Iosuh2aIqiKIpqpby9vYU+bmixYWZmxsnKRUtwdHTE48ePERgYiJ49e7JtWaKiouDu7o7MzMyWfomfbfTo0ZgxYwYmT56M//3vf0hPT4erqyv279+P58+f49q1a5xlN251Iy7iykxOTsbRo0ebLDbGRV9iPp+PHj16ICsrix3r2bMnJ22nPrTFmsfjET9vPWTIECgpKSE0NJRtGfT8+XPMmDEDZWVlzT5E+reaq2IO1L8/ktXvKepddCWYoiiKolopZ2dnqKioNHktKSlJZGsvaX/++Sfs7OyIF8VqLDo6GlFRUSLvU0dHB4WFhZzlAoCtrS0CAwOFVva4sGHDBlRUVAAAfHx84OjoiHnz5kFHRwfBwcGcZn+tDh8+DEdHR1haWiI6OhqjR49Gbm4uHj16xFmRs9jYWKEq5gAQFhaGqqoq4ln5+fnE7/khqampSE5OFuqZq6CgAB8fH7YCNkkt8R5jYmIQExPTZEsm0j+L4uyBTn06OgmmKIqiqFZq9OjRuHLlisj536tXr8LW1hbl5eWc5s+ZMweDBg3idEWvsrJSZGIB1G+TbtiuyJX4+HjOCwwxDANlZWV2K6mysjLOnz/PaWZjw4cPF3ufUnFkbtiwAX5+fliwYAHk5OSwfft2dO/eHXPmzOHsoUZTZ6C5mBwC9UWcxE1XVxePHj1C7969hcYfP34MbW1tznKrq6uRn58PLS0tTs4eN/D29sbatWthbGzMngvmUnObbd++fStUDZ9qGXQSTFEURVGt1ODBgzF69GjExsZCTk4OwP/1D/Xy8uI8XxwnpoYPH46wsDCsW7cOQP02yLq6OmzevPm9FYC/FC3dLzQyMvKrzLx37x5sbW0B1LfUqqysBI/Hw+LFi2Fubi5ylOBLc+bMmSbHeTwepKSkoK2t/d7txP/Gxo0b4erqCi8vL6FzyGvXrsWmTZuEqtGTqOZeVVUFFxcXhIaGAgByc3OhqakJFxcXdOvWjfg5XX9/f+zbt6/Jugck7dixA0D931VgYCBkZWXZa7W1tYiPj/9qjrN8yegkmKIoiqJaqcDAQPzwww8YO3YsoqKikJCQADs7O6xfv77ZHsJfms2bN2PUqFFITk5GdXU1li1bhszMTDx79gxXr17lNFtdXV2kGA9p4uoX2tykqSmkKn23RGYDBQUFdot5t27dkJGRAX19fZSXlxPbnmxkZISYmBgoKCiIFKl7182bN4lkNrC3t2fP/TbW+CywiYkJTp06JbR9+XM0VEifOHEi+14b8ht6bJM8h7x8+XKkpaXh8uXLQhW3LSws4OXlRXwSXF1djaFDhxK9Z1P8/PwA1H+t/P392T7PQP0DGw0NDU6KDFKfhk6CKYqiKKqV4vP5OHz4MGxtbWFubo709HRs3LgRCxcuFEv+uXPn0LVrV04z+vTpg9zcXOzcuRNycnJ49eoVxo8fjwULFnCyrbWoqAiqqqrg8XjIyMhgxxmGQXFxMfEqvwDg6+uLpUuXctov1N7e/qM+j2QhpZbIbGBqaooLFy5AX18fDg4OcHNzw6VLl3DhwgWMGjWKSMa4cePYLfkf+15JuXDhAlasWAEfHx8MHDgQAHD9+nWsWrUKK1euRIcOHTBnzhz8/PPPCAoKIpIp7rZTp06dwpEjRzB48GChBwy9e/fGvXv3iOfNmjULhw4dwqpVq4jfu7GGs84jR47EyZMniT2koMii1aEpiqIoqhVp3PqkQUVFBSZNmgRbW1vMmzePHSfVGuW/pCWK1SgoKKCqqgoCgYD2CyXk2bNnePPmDbp27cpun09ISICOjg5Wrlz5xU88+vTpgz/++ENk5fLq1auYPXs2MjMzcfHiRcycORNFRUXEcsvLyxEUFCTUJ9jZ2RkdOnQgltFARkYGGRkZ0NTUFKoonpaWBlNTU7x48YJonpubG8LCwmBgYAADAwORXSBbt24lmveu2tpa3L59G+rq6l/89+fXgE6CKYqiKKoV4fP5ItsgG3/MRWuUxrp37/7ebZ9ctC0pLy/H9evXm6zY6ujoSDSLz+fj0aNHUFJSEhovLCxEr169UFlZSTQPAPbt2/fer+n06dOJZ1JfNmlpady4cUNk58Dt27cxcOBAvH79GoWFhejZsyex7d/JycmwsrKClJQUu/p848YNvH79GtHR0TAyMiKS08DU1BQODg5wcXGBnJwc0tPT0b17d7i4uCAvL494Abn31Rjg8Xi4dOkS0bxFixZBX18fzs7OqK2thampKRITEyEjI4O//voLZmZmRPOoT0O3Q1MURVFUK9ISbUMaW7RokdDHNTU1uHXrFs6fP4+lS5cSz4uIiMCUKVPw6tUryMvLC00WeTwesUmwu7s7e89Vq1YJVaSura3FtWvX0K9fPyJZ75oxYwYn932fyspKxMXFNdlD19XV9YvMbFyY6UNIFG5qrLa2Fn5+fs32JSa9mt+/f38sXboUYWFh7AObsrIyLFu2jK1InZeXB1VVVWKZixcvxtixYxEQEMBWaRYIBJg1axYWLVqE+Ph4YllAfYVva2trZGVlQSAQYPv27cjKykJCQgLi4uKIZgHi3+597NgxTJ06FUD9v3MFBQXIzs7G/v37sWLFCs5rHlDvR1eCKYqiKIr6oF27diE5ORkhISFE76urqwsbGxts2LChyVZJpDSsAsXFxWHIkCFCLUoaitX8/PPPnBSvEvcW7Fu3bsHGxgZVVVWorKyEoqIinjx5AhkZGSgrK3Oymi+OzIZdEu/D1S6J1atXIzAwEEuWLMHKlSuxYsUKFBQU4NSpU1i9ejXxBws5OTkYN24c8vPz2YlucXExNDU1cfr0aejq6uLUqVOoqKggVu1YWloat27dEqlcnJWVBWNjY076Id+7dw++vr5IS0vDq1evYGRkBA8PD+jr6xPPEjcpKSncvXsXKioqmD17NmRkZLBt2zbk5+ejb9++n/RQhyKPToIpiqIoqhU5c+YMrK2tISEh8cHqu6Qr7r7P/fv30a9fP+K/uLVv3x63b9/mtBdxY05OTti+fTvxlcL34fP5KC0tFZkE//PPP9DS0iLeq9jMzAy6urrw9/dHhw4dkJaWBgkJCUydOhVubm4YP3480TxxZX7K6mBTPX0/h5aWFnbs2AFbW1vIyckhNTWVHUtKSsKhQ4eI5gFAXV0doqOjkZubCwDQ09PDd999Bz6fTzwLAL799lvs378fo0ePFhqPioqCo6MjHj16xEku1z72e+/kyZNEc9XV1REQEIBRo0ahe/fu2LNnD2xtbZGZmQkTExM8f/6caB71aeh2aIqiKIpqRezt7dkJ0/sq0nJ1Jrg5x48fh6KiIvH7WlpaIjk5WWyT4IaV7Lt37+LevXswNTWFtLQ0u4JIUkv1C01NTcXevXvB5/PRpk0bvH37Fpqamti8eTOmT5/OySRYHJmkJ7aforS0lF2dlJWVZYs2jRkzhrNqw3w+H1ZWVkLtg7j0448/wtnZGVu2bGELcl29ehVLly7FpEmTOMmsra1FeHi4UCGucePGsduxSeCiqNfHcHJywsSJE9GlSxfweDxYWFgAAK5du0b7BLcCdBJMURRFUa1I48JQ7xaJEod3+6EyDIPS0lKUlZVh9+7dxPNsbW2xdOlSZGVlQV9fX6RiK+nV7mfPnsHBwQGxsbHg8XjIy8uDpqYmnJ2doaCggN9++41YVkv1C5WQkGBXC5WVlVFUVISePXuiQ4cOKC4uJp7XUpkN9PX1ERkZSfR87LtUVFRQUlICNTU1aGlpsYWibty4wbZRIq2oqAgSEhJCrcJKSkpQU1PDSSuvLVu2sOfwBQIBgPq/13nz5sHX15d4XmZmJuzs7FBaWgo9PT0AwKZNm6CkpISIiAhi7cRIH+H4WF5eXujTpw+Ki4vh4ODAfp+0adOGeA9k6tPR7dAURVEURbG8vb2FPubz+VBSUoKZmRknqxfv29rJxWq3o6MjHj9+jMDAQPTs2ZNtyxIVFQV3d3dkZmYSzQPE3y909OjRmDFjBiZPnoz//e9/SE9Ph6urK/bv34/nz5/j2rVrX0Vmg8btdbji6ekJeXl5/PLLLzhy5AimTp0KDQ0NFBUVYfHixZxMEvl8Pnr06IGsrCx2rGfPnsjNzeV0F0hVVRXbp1dLS4uzs/pDhgyBkpISQkND2Z+N58+fY8aMGSgrK0NCQgInuQDw559/ws7ODu3bt+csg2rd6CSYoiiKoloxcVf5ffDgAVRUVJq8lpSUhMGDBxPPFKfOnTsjKioKffv2FZo83b9/HwYGBnj16hXxzDdv3kBKSqrJayUlJUIrfSQkJyejoqICI0eOxOPHj+Ho6Mj20A0ODkbfvn2J5rVUZgNxTILflZiYiMTEROjo6GDs2LGcZMTFxUFGRoatBg3Utyyqqqpq0a3hpEhLSyM5ORm9e/cWGs/IyMCAAQOIn5VvTF5eHqmpqZx/z8TExCAmJqbJ9m/BwcGcZlPvR7dDUxRFUVQr9aGKu1xMgkePHo0rV66InP+9evUqbG1tUV5eTjxTnCorK5tc2Xr27Bln21qNjIxw6NAhkRZMJ06cwNy5c1FWVkYsi2EYKCsrs1tJlZWVifdbbQ2ZjQ0fPhzS0tJiywPqVzGHDBnCaUZTE93GE+Ivna6uLh49eiQyCX78+DG0tbU5zRbHGqC3tzfWrl0LY2Nj9lww1XrQSTBFURRFtVINfTsbKu4mJSUJVdzlwuDBgzF69GjExsZCTk4OABAfH4+xY8fCy8uLk0xxrpYMHz4cYWFhWLduHYD6Ldd1dXXYvHkz20aJNDMzMwwePBje3t7w8PBAZWUlFixYgKNHj8LHx4doFsMw0NbWRmZmJiftnlpLZmORkZFiycnLy0NsbGyT36erV68Wy2v4mmzcuBGurq7w8vJid5gkJSVh7dq12LRpk1AlenFWcyfF398f+/btI9bCiiKLboemKIqiqFaqY8eOuHbtGvT09NCxY0ckJiaiZ8+euHbtGqZPn47s7GzimXV1dfjhhx/w7NkzREVFISEhAXZ2dli/fj0nE+8PrZaEh4cTzcvIyMCoUaNgZGSES5cuwc7ODpmZmXj27BmuXr0KLS0tonkNzp49i1mzZkFbWxslJSWQlZXFgQMHiBX/aax3794ICgoS69Z1cWR+qGVYY6QLqgUEBGDevHno1KkTOnfuLPR9yuPxcPPmzc/OMDIyQkxMDBQUFEQK1L2LRF5La1wPoOG9NkxLGn/MRW2AK1euwNjYuNljCiR88803uH79Omf/plCfh64EUxRFUVQr1RIVd/l8Pg4fPgxbW1uYm5sjPT0dGzduxMKFCznJE/dqSZ8+fZCbm4udO3dCTk4Or169wvjx47FgwQLiZ3Mbs7a2xvjx47Fnzx60bduWaPXbd/n6+mLp0qXYs2cPZxktkfm+lmGNcTFpWr9+PXx8fODh4UH0vo2NGzeO3ZL/se/1SxYbG9ti2SYmJpxnzJo1C4cOHeKshRb1eehKMEVRFEW1UuKquJueni4yVlFRgUmTJsHW1hbz5s1jxw0MDIhkNvgvrJbcu3cPkydPRmlpKQIDAxEXF4dff/0Vbm5u8PHxEWkL9bkUFBRQVVUFgUAASUlJkfOyz549I5rXUpniJK5CSv815eXlCAoKEuoT7OzszElv30ePHuHnn39mj168OwUi/eDEzc0NYWFhMDAwgIGBgcjP+datW4nmUZ+GToIpiqIoqpUSV8VdPp8PHo8n9Eth448b/puLFTYPDw/IysqKdbWkvLwc169fb/Jsp6OjI/E8OTk52Nrawt/fHx07dgQAJCQkwNHREXJycrh16xbRvH379r13K+306dOJ5rVUpjg5OztjwIABmDt3bku/lK9GcnIyrKysICUlhYEDBwKor379+vVrtg8zSdbW1igqKsLChQubPHoxbtw4onnvqzHA4/Fw6dIlonnUp6GTYIqiKIr6jyssLPzoz1VXVyeaLe7VkoiICEyZMgWvXr2CvLy8yNlOLlYs9+/f3+R274qKCixatAhBQUHEM/8LxNk+bOPGjdi6dStsbW2hr68v8n1KOq+2thZ+fn44evRok+/vS19ZB+qL1GlrayMgIABt29af0BQIBJg1axbu37+P+Ph4onlycnL4+++/Raq0U/9NdBJMURRFUVSLEfdqia6uLmxsbLBhw4YmWyV9Ddq0aYOSkhIoKysLjT99+hTKysrEV/NbIvND7cPu379PNK979+7NXuPxeMTzVq9ejcDAQCxZsgQrV67EihUrUFBQgFOnTmH16tWctEcTN2lpady6dQs9evQQGs/KyoKxsTGqqqqI5vXq1QsHDx6EoaEh0ftSXyZaGIuiKIqiWilxnWE7c+YMrK2tISEh8cEKvKSr7oq7OM7Dhw/h6urK+QT4Y7+mPB4PY8eOJZrd3PrG27dvISkpSTSrpTLF3T4sPz+f+D3f5+DBgwgICICtrS28vLwwadIkaGlpwcDAAElJSV/FJFheXh5FRUUik+Di4mK2PRtJ27Ztg6enJ/bu3QsNDQ3i928wfvz4j/q8kydPcvYaqA+jk2CKoiiKaqVmzJiBoqIirFq1qskzbKTY29ujtLQUysrK761Ky8WZ4MYePHgAAFBRUeEsw9LSEsnJyZwXOGqJr+mOHTvYewYGBkJWVpa9Vltbi/j4eJEJx5eYCQCpqanYu3cv+Hw+2rRpg7dv30JTUxObN2/G9OnTP3oi8qmqq6uRn58PLS0tdgsvF0pLS6Gvrw8AkJWVxYsXLwAAY8aM+WqqDf/4449wdnbGli1bMHToUADA1atXsXTpUkyaNImTvKqqKmhpaUFGRkZkSzupLeZcFPWiyKOTYIqiKIpqpa5cuSKWM2yNi0O9WyiKa3V1dVi/fj1+++03vHr1CkD92b0lS5ZgxYoVQr1ESbC1tcXSpUuRlZXV5NlOUivdLfE19fPzA1C/Kuvv7482bdqw1yQlJaGhoQF/f/8vPhMQf/uwqqoquLi4IDQ0FACQm5sLTU1NuLi4oFu3bvD09CSap6KigpKSEqipqUFLS4stFHXjxg22jdKXbsuWLeDxeHB0dIRAIABQ//c6b948+Pr6Es/btm0b8Xs2JSQkRCw51OehZ4IpiqIoqpX6L5xhW758OYKCguDt7Y1hw4YBqJ/8e3l54X//+x98fHyI5r1vUs31Sre4jBw5EidPnoSCgsJXmymu9mEN3NzccPXqVWzbtg1WVlZIT0+HpqYmTp8+DS8vL+IVvj09PSEvL49ffvkFR44cwdSpU6GhoYGioiIsXryYk0liS6mqqsK9e/cAgF2l/dr8+eefsLOzQ/v27Vv6pVD/H50EUxRFUVQrFR0djd9++43zM2zvEmfV3a5du8Lf319kBfb06dOYP38+Hj58SDSvpcTExMDPz4/th9qzZ08sWrQIFhYWxLPevHkDKSmpJq+VlJSgS5cuX3ymuNqHNVBXV8eRI0cwePBgyMnJIS0tDZqamrh79y6MjIzw8uVLonnvSkxMRGJiInR0dIifIf8vqa2txalTp9ifw969e8POzk5oBwMXaJ/p1odOgimKoiiqFVFQUBA6+1tZWQmBQMDpGbbGxF11V0pKCunp6dDV1RUaz8nJQb9+/fD69WuieS1h9+7dcHNzww8//IAhQ4YAAJKSknD8+HH4+flhwYIFRPN69eqFQ4cOiWyjP3HiBObOnYuysjKieeLOZBgGxcXFUFZWbnbiTZqMjAwyMjKgqakpNAlOS0uDqakpe2aXar3u3r0LGxsbPHz4EHp6egDq/51RVVXF2bNnoaWlxVl24+8ZqnWgZ4IpiqIoqhUR17m15oi76m7fvn2xc+dOtsBSg507dxJfzWsQExPDVtx+97xucHAw8bwNGzbAz88PCxcuZMdcXV0xbNgwbNiwgfgk2MzMDIMHD4a3tzc8PDxQWVmJBQsW4OjRo8S3l7dEJsMw0NbWRmZmJnR0dIjeuznGxsY4e/YsXFxcAIB9UBUYGMg+2CAtLy8PsbGxTX6frl69mpPMr5mrqyu0tLSQlJQERUVFAPUtvKZOnQpXV1ecPXu2hV8hJU50JZiiKIqiKFbHjh1x7do16OnpoWPHjkhMTETPnj1x7do1TJ8+HdnZ2UTz4uLiYGtrCzU1NXYykZiYiOLiYkRGRmL48OFE87y9vbF27VoYGxs3WXE7PDycaB5QX903NTUV2traQuN5eXkwNDRkC4KRdPbsWcyaNQva2tooKSmBrKwsDhw4gD59+hDPaonM3r17IygoCIMHDyZ+76ZcuXIF1tbWmDp1Kvbt24c5c+YgKysLCQkJiIuLQ//+/YnmBQQEYN68eejUqRM6d+4s9H3K4/Fw8+ZNonn/Be3bt0dSUhJbdbtBWloahg0bxsnPYYMrV67A2NhYbDsXqA+jK8EURVEU9QWwtbVFYGAgJ+c5GxN31d0RI0YgNzcXu3btYifY48ePx/z589G1a1fief7+/ti3bx+mTZtG/N7NsbOzQ3h4OJYuXSo0fvr0aYwZM4aTTGtra4wfPx579uxB27ZtERERwekEWNyZvr6+WLp0Kfbs2cP5+wIAExMTpKamwtfXF/r6+my15sTERJFJFQnr16+Hj48PPDw8iN/7v6pdu3aoqKgQGX/16hVn/bMbmJiYcHp/6tPRlWCKoiiK+gKI60yZuKvuits333yD69evc3r+713r16/Hli1bMGzYMKEzwVevXsWSJUsgLy/Pfi6JwmP37t3D5MmTUVpaisDAQMTFxeHXX3+Fm5sbfHx8RM6WkyDuTAUFBVRVVUEgEEBSUhLS0tJC17k4Ly9OtJASeY6Ojrh58yaCgoIwcOBAAMC1a9fwv//9D/3798e+ffuI5j169Ag///wze/Ti3SnX11CJ/ktGJ8EURVEU9QUQ1yRY3FV3z58/D1lZWXalZNeuXQgICECvXr2wa9cu4i13PDw8ICsri1WrVhG97/t07979oz6Px+MRKTwmJycHW1tb+Pv7o2PHjgCAhIQEODo6Qk5Ojng7n5bI3Ldvn8hW9samT59ONA+on7SEh4ezlYV79eqFcePGoW1b8hsrnZ2dMWDAAMydO5f4vf+rysvLMX36dERERLAPZQQCAezs7LBv3z506NCBaJ61tTWKioqwcOHCJo9ejBs3jmge9WnoJJiiKIqivgB9+vTBuXPnoKqq2tIvhSh9fX1s2rQJNjY2uH37NoyNjbFkyRLExsaiR48eCAkJIZrn5uaGsLAwGBgYwMDAQGSFcuvWrUTzWsL+/fub3O5dUVGBRYsWISgo6KvIFKfMzEzY2dmhtLSUrSycm5sLJSUlTrZ9b9y4EVu3boWtrS309fVFvk9Jtyr7L8nLy2OPXvTs2VPkrD4pcnJy+Pvvv0UqplOtA50EUxRFURTVYmRlZZGRkQENDQ14eXkhIyMDx48fx82bN2FjY4PS0lKieSNHjmz2Go/Hw6VLl4jmUdxo06YNSkpKoKysLDT+9OlTKCsrE99qOmTIECgpKSE0NJTdnfD8+XPMmDEDZWVlSEhIIJr3vt0DpHYMUNzq1asXDh48CENDw5Z+KVQTaGEsiqIoimqFGIZBQUEBVFVV0bZtW1RXVyM8PBxv376FjY0NOnXqxEmuuM+xSUpKoqqqCgBw8eJFODo6AgAUFRXx8uVLolkAEBsbS/yeHyM+Ph4yMjIwNjZmx5KTk1FVVQVTU9PPvv+ZM2dgbW0NCQkJnDlzptnP4/F4GDt27GfntVRmg+bWcN6+fctJkaPU1FQkJycLbc9XUFCAj48PBgwYQDwvPz+f+D3/i9zd3bFu3Tq0b98e7u7u7/1c0rtAtm3bBk9PT+zduxcaGhpE7019PjoJpiiKoqhWJicnB5aWliguLoampiaio6Ph4OCA7OxsMAwDGRkZ9pwuaTNmzEBRURFWrVrV5Dk20kxMTODu7o5hw4bh+vXrOHLkCID6raYqKiqcZj948AAAOM8B6vvo9ujRA1lZWezYtGnTkJubS+TBgr29PUpLS6GsrAx7e/tmP4/H4xF7kNESmQ39pHk8HgIDAyErK8teq62tRXx8PHr06EEkqzFdXV08evQIvXv3Fhp//PgxZ9tpAaC6uhr5+fnQ0tLi5Ozx1+7WraD5RFEAAHAwSURBVFuoqalh/7s5XPw79+OPP6KqqgpaWlqQkZER2dL+pRdv+9LR7dAURVEU1crY29uDYRisX78ewcHBiIqKgq6uLo4dO4a6ujo4ODigQ4cO2L9/P/FscZ9jKyoqwvz581FcXAxXV1c4OzsDABYvXoza2lp20kNKXV0d1q9fj99++43tCyonJ4clS5ZgxYoVbHso0goLCyEhISHU9umff/5BTU0N1NXVOcn8GjVsEy4sLISKigratGnDXpOUlISGhgbWrl2LQYMGEc2NjIzEsmXL4OXlxfYmTkpKwtq1a+Hr6yvUAqdxte9/q6qqCi4uLggNDQVQ/1BIU1MTLi4u6NatGzw9PT87g+JWw99dc7go3kZ9PDoJpiiKoqhWRllZGdHR0ejXrx8qKyshJyeH+Ph49hfthIQETJo0CYWFhcSzv/ZzbMuXL0dQUBC8vb0xbNgwAMCVK1fg5eWF//3vf/Dx8WnhV0h9jJEjR+LkyZPEq4c3p/HDkYZVw4ZfoRt/TGrV283NDVevXsW2bdtgZWWF9PR0aGpq4vTp0/Dy8uKkwvd/zcuXL3Hp0iX06NGDk90DVOtGJ8EURVEU1crIyMggOzsbampqAOpXKlNTU9netsXFxdDR0cGbN2+IZ0dHR+O3335rkXNstra2CAwMRJcuXTjL6Nq1K/z9/WFnZyc0fvr0acyfPx8PHz7kLFucYmJi4Ofnx7bz6dmzJxYtWgQLC4uvIvPNmzeQkpJq8lpJSQnx76G4uLiP/twRI0Z8dp66ujqOHDmCwYMHC7VHu3v3LoyMjDg5L/+1mzhxIkxNTbFw4UK8fv0affv2RUFBARiGweHDhzFhwgTimbW1tTh16hT7M9G7d2/Y2dkJ7WCgWgY9XEBRFEVRrUzXrl1RVFTEToI3b94sVAW3rKyM6AqYgoKC0Jm4ysrKFjnHFh8fj9evX3N2f6D+9Te16tOjRw+i7+3dr+mHXhNJu3fvhpubG3744Qe4ubkBqN+6a2NjAz8/PyxYsIBoXktkGhkZ4dChQyLb9k+cOIG5c+eirKyMaN6IESNQXl6OoKAgoT7Bzs7OxPvLAvU/4+9Wvgbqfza5Pqf/tYqPj8eKFSsAAOHh4WAYBuXl5QgNDcX69euJT4Lv3r0LGxsbPHz4kG2rtXHjRqiqquLs2bPsQ02qZdCVYIqiKIpqZebOnQtjY2PMmjWryeu+vr74+++/cfbsWSJ5Hzq71hiX59gar3hxZdCgQRg0aJDIWWMXFxfcuHEDSUlJRHJa8muqoqICT09PLFy4UGh8165d2LBhAyer3eLOnD9/PoKDg+Ht7Q0PDw9UVlZiwYIFOHr0KHx8fLB48WKiecnJybCysoKUlBQGDhwIALhx4wZev36N6OhoGBkZEc0zNTWFg4MDXFxcICcnh/T0dHTv3h0uLi7Iy8vD+fPnieb9F0hLSyM3NxeqqqpwdHRE165d4evri6KiIvTq1YutEUCKjY0NGIbBwYMHoaioCKC+hdfUqVPB5/OJ/ftN/Tt0EkxRFEVRX5j8/HxISUlxum24JfTp0wfnzp2DqqoqZxlxcXGwtbWFmpoahgwZAgBITExEcXExIiMjMXz4cM6yxUVWVhapqakiVYvz8vJgaGhI/Jf9lso8e/YsZs2aBW1tbZSUlEBWVhYHDhxAnz59iGcNHz4c2traCAgIYKs0CwQCzJo1C/fv30d8fDzRvCtXrsDa2hpTp07Fvn37MGfOHGRlZSEhIQFxcXHo378/0bz/Al1dXaxfvx62trbo3r07Dh8+DHNzc6SlpWHUqFF48uQJ0bz27dsjKSkJ+vr6QuNpaWkYNmwYJz8T1MfjpgQiRVEURVFEPXjwAHV1dQDqK+SKYwJsa2uLkpISznMaZGRkcDoBBuq3tebm5uL7779HeXk5ysvLMX78eOTk5IhlAvzmzRu8fPlS6A9pdnZ2CA8PFxk/ffo0xowZQzyvpTKtra0xfvx4XL16FUVFRdi0aRMnE2CgfiXYw8NDqE1R27ZtsWzZMiQnJxPPMzExQWpqKgQCAfT19REdHQ1lZWUkJibSCfC/tGjRIkyZMgUqKiro2rUrzMzMANRvk353okpCu3btUFFRITL+6tUrTnpZU5+GrgRTFEVR1BdAXl4eqampnG4VfhfX25MZhkFBQQFUVVXRtm1bVFdXIzw8HG/fvoWNjQ06derESa64VVZWwsPDA0ePHsXTp09FrpPqodtg/fr12LJlC4YNG8audiclJeHq1atYsmSJUAsfV1fXLzLz3r17mDx5MkpLSxEYGIi4uDj8+uuvcHNzg4+Pj8hZ9s/17bffYv/+/Rg9erTQeFRUFBwdHfHo0SOieRQ3kpOTUVxcjO+++47tMX327Fl07NiRrRZPiqOjI27evImgoCB2C/21a9fwv//9D/3798e+ffuI5lGfhk6CKYqiKOoLII7zsuLMzMnJgaWlJYqLi6GpqYno6Gg4ODggOzsbDMNARkYGCQkJ0NHRIZp7/vx5yMrKsu2mdu3ahYCAAPTq1Qu7du3ipOXOggULEBsbi3Xr1mHatGnYtWsXHj58iL1798LX1xdTpkwhmtfQS/dDeDwe7t+//0VmysnJwdbWFv7+/ujYsSOA+tZhjo6OkJOTI95CyNXVFeHh4diyZQuGDh0KALh69SqWLl2KCRMmYNu2bUTzgPqHI+Hh4UKFuMaNGye0Gk21XuXl5Zg+fToiIiLYhzICgQB2dnbYt28fJwXVqI9HJ8EURVEU9QVoiUkwl2d07e3twTAM1q9fj+DgYERFRUFXVxfHjh1DXV0dHBwc0KFDB+zfv59orr6+PjZt2gQbGxvcvn0bxsbGWLJkCWJjY9GjRw+EhIQQzQMANTU1hIWFwczMDPLy8rh58ya0tbWxf/9+/Pnnn4iMjCSe+bXbv38/pk2bJjJeUVGBRYsWISgoiGhedXU1li5dCn9/fwgEAgCAhIQE5s2bB19fX7Rr145oXmZmJuzs7FBaWspWFs7NzYWSkhIiIiI42/b9tXF3d8e6devQvn17uLu7v/dzt27dyslryMvLQ3Z2NoD6tmHvnpunWgadBFMURVHUF2Djxo2YN28eu+r1pVNWVkZ0dDT69euHyspKyMnJIT4+nl2hTUhIwKRJk1BYWEg0V1ZWFhkZGdDQ0ICXlxcyMjJw/Phx3Lx5EzY2NigtLSWa15CZlZUFNTU1qKio4OTJkxg4cCDy8/Ohr69PC+R8QaqqqnDv3j0AYNuIcWHIkCFQUlJCaGgouzvh+fPnmDFjBsrKypCQkMBJ7tdm5MiRCA8PR8eOHTFy5MhmP4/H4+HSpUtifGVUS6P7KSiKoiiqlauursb48ePZM2xcEecZ3VevXrFtQ9q3b4/27dsLFftSVVXl5JylpKQkqqqqAAAXL16Eo6MjAEBRUZGTIlUAoKmpifz8fKipqaFHjx44evQoBg4ciIiICM4easTHx0NGRgbGxsbsWHJyMqqqqmBqavpFZp45cwbW1taQkJDAmTNnmv08Ho+HsWPHfnZeU2RkZDgpovSu1NRUJCcnC23PV1BQgI+PDwYMGMB5/tciNjaWPXMfGxvLeV5rWHmmPg6dBFMURVFUK1VVVQUXFxe252xubi40NTXh4uKCbt26wdPTk1iWuM/odu3aFUVFRVBTUwMAbN68GcrKyuz1srIyTs7nmpiYwN3dHcOGDcP169dx5MgRAPVfWxUVFeJ5AODk5IS0tDSMGDECnp6eGDt2LHbu3ImamhrOfhE2MzNDjx49kJWVxY5NmzYNubm5xAtxiSvT3t4epaWlUFZWhr29fbOfx+PxOHuP4qKrq4tHjx6hd+/eQuOPHz+m22k/kYqKCqZPnw5nZ2fiNQbedevWLdTU1LD/3Rwej8fp66A+jG6HpiiKoqhWys3NDVevXsW2bdtgZWWF9PR0aGpq4vTp0/Dy8iJa/EfcZ3Tnzp0LY2NjzJo1q8nrvr6++Pvvv3H27FkieQ2Kioowf/58FBcXw9XVFc7OzgCAxYsXo7a2Fjt27CCa15TCwkKkpKRAW1sbBgYGnGVISEiga9eu7Ng///yDmpoaqKurfzWZX6vIyEgsW7YMXl5eGDx4MID6attr166Fr68ve2wAgFDlbUrUunXrEBoaivz8fAwdOhTOzs6YOHEiZ1vZqS8DnQRTFEVRVCulrq6OI0eOYPDgwUKFse7evQsjIyOi23db6oxuc/Lz8yElJSWWfshcqqmpgZWVFfz9/TlfhaK+Hnw+n/3vhlXDhl/ZG3/8Nax6i8vly5cREhKCEydOoE2bNpg4cSJmzZqFQYMGiSX/5cuXuHTpEnr06IEePXqIJZNqHt0OTVEURVGtVFlZmdAW4QaVlZXEt9O11Bndxh48eICuXbuCz+d/dMudz2Fra4vAwEBOJ9oSEhJIT0/n7P7/ZTExMfDz82NbCPXs2ROLFi2ChYVFC7+yzyeO86v/NWZmZjAzM8OuXbtw+PBh7Nu3D0OGDEHPnj3h7Oz8wTO8n2rixIkwNTXFwoUL8fr1axgbG6OgoAAMw+Dw4cOYMGEC0Tzq0/A//CkURVEURbUEY2Njoe3ADRPfwMBADBkyhGhWwxndBuI6o9tYr169UFBQwGlGY/Hx8Xj9+jXnOVOnTiXesuddCgoKUFRU/Kg/X3Jmg927d8PKygpycnJwc3ODm5sb5OXlYWNjg127dhHPE7cRI0agb9++SE5Oxv79+7F//36kpKSgX79+GDFihNAf6tPIyspi1qxZuHLlCiIiIlBaWoqlS5cSz4mPj8fw4cMBAOHh4WAYBuXl5dixYwfWr19PPI/6NHQlmKIoiqJaqQ0bNsDa2hpZWVkQCATYvn07srKykJCQgLi4OKJZFhYWyM7OZrc/z5s3T+h6dHQ0jIyMiGa+62s9oSUQCBAcHIyLFy+if//+aN++vdB1EsWxtm3b9tn3+BIyG2zYsAF+fn5YuHAhO+bq6ophw4Zhw4YNWLBgQYu9NhKSk5NhZWUFKSkpDBw4EADg5+eHDRs2iOVn8WtWVVWFo0ePIiQkBFeuXIGWlhYnk+AXL16wD4DOnz+PCRMmQEZGBra2tpzkUZ+GngmmKIqiqFbs3r178PX1RVpaGl69egUjIyN4eHiIpU1LY+I4o9v43LM49OnTB+fOnYOqqiqnOe/rTwrQra//hqysLFJTU0UqJefl5cHQ0PCL7708fPhwaGtrIyAgAG3b1q9ZCQQCzJo1C/fv30d8fHwLv8IvT0JCAoKDg3Hs2DEIBAL88MMPcHZ25qxlmK6uLtavXw9bW1t0794dhw8fhrm5OdLS0jBq1Cg8efKEk1zq49BJMEVRFEVRTWp8RlccNm7ciHnz5nHWO/e/6M2bN6iurhYa47qasDgyJ0+eDENDQ5EVtS1btiA5ORmHDx8mmidu0tLSuHXrlkgBpaysLBgbG7O9rqkP27x5M0JCQpCbmwtjY2M4Oztj0qRJkJOT4zR39+7dcHNzg6ysLNTV1XHz5k3w+Xz8/vvvOHnyJH341cLoJJiiKIqiWpFPqfjM9WRGXl4eqampYlmZra6uRn5+PrS0tNiVL9IYhkFBQQFUVVXRtm1bVFdXIzw8HG/fvoWNjQ06derESe7MmTOxfft2kV+6Kysr4eLiguDgYKJ5lZWV8PDwwNGjR/H06VOR61xUExZ35vr167FlyxYMGzaMPR+flJSEq1evYsmSJUI/G66urkSzxeHbb7/F/v37MXr0aKHxqKgoODo6cl6k7muipKSEqVOnwtnZGX369BFrdnJyMoqLi/Hdd99BVlYWAHD27Fl07NgRw4YNE+troYTRSTBFURRFtSJ8Pv+jKz9z3RpFHNuTq6qq4OLigtDQUABAbm4uNDU14eLigm7dusHT05NITk5ODiwtLVFcXAxNTU1ER0fDwcEB2dnZYBgGMjIySEhI4KSNUZs2bVBSUiJS6fvJkyfo3LkzBAIB0bwFCxYgNjYW69atw7Rp07Br1y48fPgQe/fuha+vL6ZMmUI0ryUyP7Z6OI/Hw/3794lmi4OrqyvCw8OxZcsWDB06FABw9epVLF26FBMmTGjR89hfmpqaGkhISAiN6evrIzIykvOjEFTrRQtjURRFUVQr0niLXEFBATw9PTFjxgx2tSsxMRGhoaHYuHFjS71EopYvX460tDRcvnwZVlZW7LiFhQW8vLyITYI9PDzQt29fREREIDg4GLa2ttDV1UViYiLq6urg4OCAtWvXYv/+/UTygPpVfYZhwDAMKioqICUlxV6rra1FZGRkky2wPldERATCwsJgZmYGJycn9nypuro6Dh48yMkkWNyZ+fn5RO/X2mzZsgU8Hg+Ojo7sQxIJCQnMmzcPvr6+LfzqvizvToCB+n9ba2pqiGe5u7tj3bp1aN++/QdbLpEoiEf9e3QSTFEURVGtSOOWJ2vXrsXWrVsxadIkdszOzg76+vr4448/MH36dE5fyy+//MJJe5vGTp06hSNHjmDw4MFCK+C9e/fGvXv3iOUkJCQgOjoa+vr6WL9+PbZv344//viD/QXZ09NT6OtMQseOHcHj8cDj8aCrqytyncfjwdvbm2gmADx79oxdvZeXl8ezZ88AACYmJiJVv7/kzK+ZpKQktm/fjo0bN7I/B1paWpCRkWnhV0a9z61bt9jJ9a1bt5r9PNJ93qlPRyfBFEVRFNVKJSYmwt/fX2Tc2NgYs2bN4jS7uroa48ePZ8+xcaWsrKzJ1dDKykqivyi+evWKndC3b98e7du3F6p0raqqSvycZWxsLBiGgbm5OU6cOCH0QEFSUhLq6uro2rUr0UwA0NTURH5+PtTU1NCjRw8cPXoUAwcOREREBGdFx1oiMz4+HjIyMjA2NmbHkpOTUVVVxVnFX3GTkZEReyX4/4Lhw4dDWlqa+H1jY2PZYyq08FXrJp5yjxRFURRFfTJVVVUEBASIjAcGBnJ2lq2qqgrOzs6QkZFB7969UVRUBABwcXHhZBumsbExzp49y37cMPENDAxkt4CT0LVrV/a9APUVYxtPvsvKyqCgoEAsD6hf1TczM0N+fj7s7e0xYsQI9s+QIUM4mQADgJOTE9LS0gDUr3Dv2rULUlJSWLx4MWf9SVsi08zMDI6OjkJj06ZN+2BLKoqKjIzkrN2biooKPD09kZeXx8n9KTJoYSyKoiiKaqUiIyMxYcIEaGtrY9CgQQCA69evIy8vDydOnICNjQ3xTDc3N1y9ehXbtm2DlZUV0tPToampidOnT8PLy+u9W/z+jStXrsDa2hpTp07Fvn37MGfOHGRlZSEhIQFxcXHo378/kZy5c+e+dwXd19cXf//9t9CEnJTz589DVlYWJiYmAIBdu3YhICAAvXr1wq5du4hPvt9VWFiIlJQUaGtrw8DAgNMscWYWFhZCQkJC6GHCP//8g5qaGqirq3OSSX25nj59im+++QYAUFxcjICAALx+/Rp2dnYYPnw4sZx169YhNDQU+fn5GDp0KJydnTFx4kS6lb2VoZNgiqIoimrFiouLsWfPHmRnZwMAevbsiblz53K2Eqyurs6e0W1cHfru3bswMjL6pBZOH+vevXvw9fVFWloaXr16BSMjI3h4eIh1G2h+fj6kpKQ4WR3S19fHpk2bYGNjg9u3b8PY2BhLlixBbGwsevTogZCQEGJZNTU1sLKygr+/PyeVrltLJkV9rNu3b2Ps2LEoLi6Gjo4ODh8+DCsrK1RWVoLP56OyshLHjx+Hvb090dzLly8jJCQEJ06cQJs2bTBx4kTMmjWLfaBJtSw6CaYoiqIoiiUjI4OMjAxoamoKTYLT0tJgamqKFy9etPRLJObBgwfo2rUr+HxuT4fJysoiIyMDGhoa8PLyQkZGBo4fP46bN2/CxsYGpaWlRPOUlJQ4a/fUmjIp6mNYW1ujbdu28PT0xP79+/HXX3/B0tKSPWri4uKClJQUJCUlcZL/6tUrHD58GPv27UNCQgJ69uwJZ2fnD1aPprhFJ8EURVEU1UrFx8e/9zoXxX9MTU3h4OAAFxcXyMnJIT09Hd27d4eLiwvy8vJw/vz5z874lNVkeXn5z857371TU1M57YMMAIqKirhy5Qp69eoFExMTODo6Yvbs2SgoKECvXr1QVVVFNG/x4sVo166dWFvpiCNTQUHho4ulNVSnpqhOnTrh0qVLMDAwwKtXryAvL48bN26wRy2ys7MxePBglJeXc/5azp49C0dHR5SXl3Pe5516P1odmqIoiqJaKTMzM5GxxpMALn6J2rBhA6ytrZGVlQWBQIDt27cLndEloaF10Mfg8hdFca0DmJiYwN3dHcOGDcP169dx5MgRAEBubi5UVFSI5wkEAgQHB+PixYvo378/2rdvL3Sdi/6k4sjctm3bZ9+D+u959uwZOnfuDKB+V0b79u2FzuErKCigoqKCs/yqqiocPXoUISEhuHLlCrS0tDgrFkd9PDoJpiiKoqhW6vnz50If19TU4NatW1i1ahV8fHw4yTQxMUFqaip8fX2hr6+P6OhoGBkZITExkdgZ3catQwoKCuDp6YkZM2aw1aATExMRGhqKjRs3EslraTt37sT8+fNx/Phx7NmzB926dQMAnDt3DlZWVsTzMjIyYGRkBKB+oi0O4sjkui829fV696GbOPr0JiQkIDg4GMeOHYNAIMAPP/yAdevWfTXtu750dDs0RVEURX1h4uLi4O7ujpSUlJZ+KZ9t1KhRmDVrFiZNmiQ0fujQIfzxxx+4fPkyZ9kbN27EvHnzOOtjS4nHmzdvUF1dLTTG5TZ66svC5/NhbW2Ndu3aAQAiIiJgbm7O7lZ4+/Ytzp8/T2zXyebNmxESEoLc3FwYGxvD2dkZkyZNgpycHJH7U2TQSTBFURRFfWGys7NhbGyMV69eEblfS57RlZGRQVpamkhBpdzcXPTr14/4edkG1dXVyM/Ph5aWFtq2Jb8x7uXLl+zX6kNfX9Jf05kzZ2L79u0iv3RXVlbCxcUFwcHBRPNaIrOyshIeHh44evQonj59KnKdnrekGjg5OX3U55Gq0q6kpISpU6fC2dkZffr0IXJPijw6CaYoiqKoVio9PV3oY4ZhUFJSAl9fXwgEAly5coVIDp/Pb7Ezunp6ehg3bhw2b94sNL5s2TKcPn0aOTk5RPOqqqrg4uKC0NBQAPWTbU1NTbi4uKBbt27w9PQkktOmTRuUlJRAWVm52a8vwzDg8XjEv6aNsxt78uQJOnfuDIFAQDSvJTIXLFiA2NhYrFu3DtOmTcOuXbvw8OFD7N27F76+vpgyZQrRPIr6WDU1NZCQkBAa09fXR2RkJGet7ahPR88EUxRFUVQr1a9fP/B4PJECToMHDya6staSZ3T9/PwwYcIEnDt3ju2fef36deTl5eHEiRPE85YvX460tDRcvnxZ6DyuhYUFvLy8iE2CL126BEVFRQDCX18uvXz5EgzDgGEYVFRUQEpKir1WW1uLyMhIkUnql5gJ1G9pDQsLg5mZGZycnDB8+HBoa2tDXV0dBw8epJNgqllv374FAHZ7NGnvToCB+n9Xa2pqOMmj/h06CaYoiqKoVio/P1/oYz6fDyUlJaGJBgkjRoxg/3vt2rXYunWr0BldOzs76Ovr448//iBenMjGxga5ubnYs2cPsrOzAQBjx47F3LlzOVk1OXXqFI4cOYLBgwcLrc727t0b9+7dI5bT+Gs6YsQIvHnzBunp6Xj8+DHq6uqI5TTWUHWbx+NBV1dX5DqPx4O3t/cXnwnUV/xtaG0lLy/PtkQyMTHBvHnziOdRX7YLFy7Az88PiYmJ7PEEeXl5DBkyBO7u7rCwsGjhV0iJG50EUxRFUVQrpa6uLvbMxMRE+Pv7i4wbGxtj1qxZnGSqqqpiw4YNnNz7XWVlZU2uTFZWVnJWMfb8+fNwdHTEkydPRK6R3A4dGxsLhmFgbm6OEydOsCvRACApKQl1dXV07dqVSFZLZgKApqYm8vPzoaamhh49euDo0aMYOHAgIiIiaKEzSkhoaChmzZqFH374AX5+fvj2228BAI8ePUJ0dDRsbGwQFBSEadOmcfYahg8fDmlpac7uT306eiaYoiiKolqxyspKxMXFoaioSKQCrqurK/E8cZ/RjY+Pf+910u1ETE1N4eDgABcXF8jJySE9PR3du3eHi4sL8vLycP78eaJ5AKCjo4PRo0dj9erV7C/gXCosLISamppY2sC0VKafnx/atGkDV1dXXLx4EWPHjgXDMKipqcHWrVvh5uYmltdBtX66urpwc3PDggULmry+e/du+Pn5IS8vT8yvjGpJdBJMURRFUa3UrVu3YGNjg6qqKlRWVkJRURFPnjyBjIwMlJWVcf/+feKZkZGRmDBhArS1tZs8o2tjY0M0j8/ni4w1nkiRLhp15coVWFtbY+rUqdi3bx/mzJmDrKwsJCQkIC4uDv379yeaB9Rvu7x16xa0tLSI37sp58+fh6ysLExMTAAAu3btQkBAAHr16oVdu3ZBQUHhq8hsrLCwECkpKdDW1oaBgQGnWdSXRUpKCmlpadDT02vyek5ODvr164fXr18TzX369Cm++eYbAEBxcTECAgLw+vVr2NnZYfjw4USzqE8n+n8eiqIoiqJahcWLF2Ps2LF4/vw5pKWlkZSUhMLCQvTv3x9btmzhJLPhjO7YsWPx7NkzPHv2DGPHjkVubi7xCTAAPH/+XOjP48ePcf78eQwYMADR0dHE80xMTJCamgqBQAB9fX1ER0dDWVkZiYmJnEyAAeCHH37gtN/xu5YuXcqee7x9+zbc3d1hY2OD/Px8uLu7f/GZNTU1GDVqlNDKnbq6OsaPH08nwJSI3r17IygoqNnrwcHB6NWrF7G827dvQ0NDA8rKyujRowdSU1MxYMAA+Pn54Y8//sDIkSNx6tQpYnnUv0NXgimKoiiqlerYsSOuXbsGPT09dOzYEYmJiejZsyeuXbuG6dOns4WkvkZxcXFwd3dHSkpKS7+Uz1ZVVQUHBwcoKSlBX19fpHos6W3tsrKyyMjIgIaGBry8vJCRkYHjx4/j5s2bsLGxQWlpKdG8lshUUlJCQkKCSH9pinrX5cuXMWbMGGhqasLCwkLoTHBMTAzu37+Ps2fPEjt6YW1tjbZt28LT0xP79+/HX3/9BUtLSwQEBAAAXFxckJKSgqSkJCJ51L9DC2NRFEVRVCslISHBbhdWVlZGUVERevbsiQ4dOqC4uJiTTHGf0W3Ot99+S+z8ccMK5ceQl5cnktnYn3/+iejoaEhJSeHy5ctC2715PB7xSbCkpCSqqqoAABcvXoSjoyMAQFFR8ZO+Fq05c+rUqQgKCoKvry/xe1NfFzMzM2RkZGDPnj1ISkpiH8h07twZ1tbWmDt3LjQ0NIjl3bhxA5cuXYKBgQH69u2LP/74A/Pnz2f/LXdxccHgwYOJ5VH/Dp0EUxRFUVQrZWhoiBs3bkBHRwcjRozA6tWr8eTJE+zfvx99+vThJNPMzExkjMszuunp6UIfMwyDkpIS+Pr6ol+/fkQyGtr4fAzS7w8AVqxYAW9vb3h6ejZ5Bpo0ExMTuLu7Y9iwYbh+/TqOHDkCAMjNzYWKispXkSkQCBAcHIyLFy+if//+aN++vdD1rVu3Es+kvlwaGhrYtGmTWLKePXuGzp07A6jfIdG+fXuhM/EKCgqoqKgQy2uhmkcnwRRFURTVSm3YsIH9ZcnHxweOjo6YN28edHR0EBwczEnm8+fPhT6uqanBrVu3sGrVKvj4+BDP69evH3g8Ht49nTV48GBi7zE2Npb974KCAnh6emLGjBkYMmQIgPq2UKGhodi4cSORvHdVV1fjxx9/FMsEGAB27tyJ+fPn4/jx49izZw+6desGADh37hysrKy+isyMjAwYGRkBqJ9oU9THKCoqQklJCfh8PjQ1NdnCVaS9+9BNnJXaqY9DzwRTFEVRFPVBXJ3RLSwsFPqYz+dDSUkJUlJSRHMajBo1CrNmzcKkSZOExg8dOoQ//viDkwJWixcvhpKSEn755Rfi96Yo6sN2796NTZs24cGDB0LjQ4YMwfbt24kWxePz+bC2tka7du0AABERETA3N2d3K7x9+xbnz5/nZNcJ9fHoJJiiKIqiqA/Kzs6GsbExXr161dIv5bPIyMggLS1NpKBSbm4u+vXr9//au/OoqK50beBPUYDIVIhQOHQzKSJGEOIUxAnjFBREO3y2xGDUmFaMEJw1BpVMzhoTozHOdowxhkTt1ogRRI2CKFBgQgTFACqKzJOCFHx/eK22UsSInBosnt9argX7VJ9n0/cumvec/e6t6GsVUlhYGPbs2YMePXrAw8NDZWMsIZbulpeXK/qZ/6oHV6i+Z21kPjJlyhR88sknsLCwUBqvqqrCrFmz1LZSgp4/a9aswfr167Fo0SKYmJhg3bp1mDBhAnr37o19+/bhu+++Q3x8PHr16iVI3uTJk5/qczt37hQkj54Ni2AiIiId4uXl9dRL55KTkwXPf1KPbl1dHc6ePSt4ZlVVFeLj45Gbm4va2lqla0JvGuXq6ooxY8Zg1apVSuPz58/HoUOHBNuM63G+vr5/ek0kEiE2NrbZGWKxGPn5+ZBKpTAwMGj0/4caGhogEokEewOljczGsh9XWFiIdu3aoa6uTtA8en45OTnh888/xyuvvALg4QOvfv364fbt2zA0NER4eDgyMjLUciQb6S72BBMREemQwMBAreZrokf3cSkpKfDz80N1dTWqqqpgbW2NwsJCmJqaQiqVCl4Er1+/Hv/4xz9w7Ngx9O3bFwBw4cIFZGVl4bvvvhM065HHe5LVJTY2FtbW1hrL01ZmeXk5Ghoa0NDQgIqKCqVl83K5HEePHlUpjKllKygogJubm+J7FxcXlJWV4e7du2jfvj2mTJmC/v37qy2/pqYGABTLo0k38E0wERERKWi6R3fw4MHo0qULtmzZAolEAplMBiMjI0ycOBHh4eEYN26c4Jl5eXnYvHmz4pxlNzc3TJ8+HX//+98Fz9KW+/fvIy0tDQUFBaivr1e6FhAQ8Nxm/tkb50dEIhGWL1+Od999V5A8ev55eXkhNDQU06ZNA/Dw4Y2/vz8qKyshEolw5coV9O7dW9CjvE6cOIH169fj/PnzivtaWlrC29sbs2fPxtChQwXLomfDIpiIiEhHJSUlob6+XvHG8pHExESIxWLBeti0ycrKComJiXB1dYWVlRXOnz8PNzc3JCYmYtKkSYpClZ7ejz/+iJCQEBQWFqpcU8fSZE1mxsfHo6GhAUOGDMF3332neBMNPDyr2MHBAR06dBAki/TDgQMHMHHiRIwdOxYmJiaIjo7G22+/rdgN/osvvsDu3btx7tw5QfJ2796NN998E6+++ipGjBgBOzs7AMCdO3cQExODgwcPYvv27Xj99dcFyaNnwyKYiIhIR/Xp0wfz58/Hq6++qjQeHR2NlStXIjExUS25muzRtbW1xblz5+Di4oIuXbrg008/xYgRI/Dbb7+hZ8+eqKqqEjTv9OnTT7w+cOBAQfO0wcXFBcOHD0dkZKTiD3B9y8zJyYG9vT2PnqGncuzYMfz73/9GTU0NRowYoXgrDABFRUUAINhxSV26dEF4eDhmzpzZ6PXPP/8c69evR1ZWliB59GxYBBMREekoc3NzpKWlwdnZWWn8+vXr8PDwUJwhLKS/6tHNzs4WNG/48OF44403EBwcjGnTpiEtLQ1hYWHYu3cvSkpKBC/0Gzur9/FCSh+OLbG0tERKSgo6deqkt5k//vgjzM3NFb2cmzZtwpdffolu3bph06ZNaNOmjUbmQfRHJiYmkMlkcHV1bfT6lStX4OnpiXv37ml4ZvQ4zZzaTkRERE3WqlUr3LlzR2U8Pz8fhobq2dsyIiIC/v7+KCkpQevWrZGQkICcnBz07NkTa9asETzvo48+Qvv27QEAH374Idq0aYMZM2bg7t272Lp1q+B5JSUlSv8KCgrw448/onfv3nqzO+yrr76qlvOOdSlz3rx5il7L9PR0zJ49G35+frh+/Tpmz56tsXnQ82nUqFHIz89Xy71feOEFbN++/U+v79ixA926dVNLNj09vgkmIiLSURMmTEB+fj4OHToEiUQCACgtLUVgYCCkUikOHDggeGZL7dGNj4/H7NmzcenSJW1Ppdmqq6sRFBQEW1tbuLu7q5xLLPSSdm1kmpub4/Lly3B0dMSyZctw+fJlHDx4EMnJyfDz88Pt27cFzSP9YmFhAZlMprLKRginTp3C6NGj4ezsjKFDhyr1BJ88eRLZ2dn473//qxetF88zHpFERESko9asWYOBAwfCwcEBXl5eAIDU1FTY2dlh7969ask0MjJSLBmWSqXIzc2Fm5sbJBIJ8vLy1JKpC+zs7NRyRrA2fP3114iJiYGJiQlOnTqltNxbJBKppQjWdKaxsTGqq6sBAD/99BNCQkIAANbW1oLu8kvUVIMHD8bly5exefNmJCQkKB7ItGvXDq+88gqmT58OR0dH7U6S+CaYiIhIl1VVVeGrr76CTCZD69at4eHhgQkTJqi8aROKJnp0vby8nnpDo+Tk5GbnPS4tLU3p+4aGBuTn52PFihWoq6vD2bNnBc3Thnbt2iEsLAwLFy5stAdaHzIDAgJQW1sLHx8fvP/++7h+/To6duyImJgYvP3228jMzFT7HOj51b17dxw7dkyvjkWjpmERTERERAoXL15ERUUFfH19UVBQgJCQEMXuzTt27ECPHj2anbF8+fKn/uzSpUubnfe4R+fM/vHPn5deegk7duxA165dBc3TBmtrayQlJWl0YyxNZ+bm5iI0NBR5eXkICwvD1KlTATzsaZfL5di4caNG5kH0JLm5ucjPz4eBgQGcnZ0F24Gamo9FMBEREbUYOTk5St8bGBjA1tYWJiYmWpqR8CIiImBra4vFixfrdSZRU9TX1ze6SqG+vh43btyAvb29YFmff/45Vq5ciRs3biiNe3t745NPPkHPnj0Fy6Jnw55gIiIiHWZgYAA3Nzf88ssvijE3NzdkZmbqxXE+SUlJqK+vR9++fZXGExMTIRaL0atXL0HzHBwcBL2fLpLL5Vi1ahWOHz8ODw8PlaXz69atey4zy8vLYWlpqfj6SR59jqi8vBxvvvkmjhw5AktLS/zrX//C0qVLIRaLAQB3796Fk5OTYL9P16xZg/Xr12PRokUwMTHBunXrMGHCBPTu3Rv79u3DwIEDER8fL/jvNmoaFsFEREQ6bMeOHbCyslIa+/jjj1FWViZYhjZ7dGfOnIn58+erFME3b97EypUrBT8nGHjYZx0fH4/c3FzU1tYqXVPHplGalp6erthI7fLly0rXnvb/zrqY2aZNG+Tn50MqlcLKyqrR+zY0NEAkEunFAyISxnvvvQeZTIa9e/eitLQUH3zwAZKTkxEdHQ1jY2MAUGmPaI5NmzZh27ZteOWVVwAAAwcORL9+/XD79m2MHDkSbdq0weLFi/XmSLbnFZdDExERtXDa7NE1NzdHWlqaylEl169fh4eHByoqKgTNS0lJgZ+fH6qrq1FVVQVra2sUFhbC1NQUUqkU2dnZguaRcOLj4+Hj4wNDQ0PEx8c/8bODBg3S0KxI1zk4OGD37t0YPHgwAKCwsBCjRo2ClZUVDh8+jNLSUnTo0EGwBydmZmb45ZdfFDtANzQ0wNjYGLm5uWjfvj1kMhn69+8v+O82ahq+CSYiItJRZWVlkMvlsLa2VhovLi6GoaGhYEs+hS5sm6JVq1a4c+eOShGcn58PQ0Ph/0yJiIiAv78/tmzZAolEgoSEBBgZGWHixIkIDw8XPI+E83hhO2jQINy/fx9paWkoKChAfX29FmdGuuzu3btKbRA2Njb46aefMGLECPj5+WHbtm2C5nXp0gUnTpzAtGnTAABxcXEwNjZGu3btAAAmJiZqW5FBT49FMBERkY765z//CX9/f4SGhiqNHzhwAIcPH8bRo0cFz9R0j+7w4cOxaNEiHDp0CBKJBABQWlqKxYsXY9iwYYJmAQ/PWf7iiy9gYGAAsViMmpoaODs7Y9WqVZg0aRLGjRsneCYJ78cff0RISAgKCwtVrnE5ND3O3t4eGRkZcHJyUoxZWFggJiYGw4cPx9ixYwXNW7RoESZOnIiffvoJJiYmiI6ORlhYmKLwPXXqFLp37y5oJjWdZg6PIyIioiZLTEyEr6+vyvjgwYPV0isLPOzRzcvLUxm/efMmZs6cKXjemjVrkJeXBwcHB/j6+sLX1xdOTk64ffs21q5dK3iekZGRYodYqVSK3NxcAIBEImn05ybdNGvWLAQFBSE/Px/19fVK/1gA0+OGDx+OnTt3qoybm5vj+PHjgu8M///+3//DoUOHYGhoiKqqKqxbtw4ff/yx4vqrr76KI0eOCJpJTceeYCIiIh1lZmaGhIQEuLu7K42np6ejb9++qK6uFjxT0z26wMONqr766ivIZDK0bt0aHh4emDBhgsoOw0IYPnw43njjDQQHB2PatGlIS0tDWFgY9u7di5KSErU9XCBhWVpaIiUlRaNnIdPzqaSkBLdu3cILL7zQ6PWKigokJyezj7yFYRFMRESko3x9fdG9e3d8+umnSuMzZ85EWloazpw5I3hm27Zt8Z///Afe3t5K4+fOncOoUaNQUlIieKYmXbx4ERUVFfD19UVBQQFCQkJw7tw5uLi4YMeOHejRo4e2p0hPYcqUKfDx8cHUqVO1PRWivzRq1Chs27YN7du31/ZU6P+wCCYiItJRP//8M4YOHYrevXvj5ZdfBgCcPHkSSUlJiImJwYABAwTPnDBhAvLz81V6dAMDAyGVSnHgwAHBM4maqrq6GkFBQbC1tYW7u7vKqgF9OOqKNOPixYuorq7GwIED1ZZhYWEBmUymssKGtIdFMBERkQ5LTU3F6tWrkZqaqlgqvGjRIri4uKgl7+bNmxg4cCCKiooU576mpqbCzs4OJ06cwN///nfBMw0MDODm5oZffvlFMebm5obMzEz2d1Kjtm/fjunTp8PExARt27ZV2m1XJBLxqCt6apr4XcMiWPewCCYiIiIlmuzRBYBdu3bBysoKgYGBirEffvgBZWVlmDRpUrPv7+Xl9dRHkiQnJzc7j9SvXbt2CAsLw8KFCxUbnRE9i1u3buHBgwdKxygJrXv37jh27JhaHiLSs2ERTEREpEPKy8sV5/+Wl5c/8bNCnROs75YvX/7Un9Xmmcn09KytrZGUlMSNsYjombAIJiIi0iFisRj5+fmQSqUwMDBo9A1mQ0OD3pyFWlZWBrlcDmtra6Xx4uJiGBoastCnRkVERMDW1haLFy/W9lToOSGXyyEWixXfJyYmoqamBt7e3mpZ5VJfX9/oKoX6+nrcuHED9vb2gmfS0zPU9gSIiIjof2JjYxUFYVxcnFbmoMke3X/+85/w9/dHaGio0viBAwdw+PBhHD16VNC8pKQk1NfXo2/fvkrjiYmJEIvF6NWrl6B5pB5yuRyrVq3C8ePH4eHhoVLErFu3TkszI12Tn5+PoKAgJCQkwMfHBz/88ANef/11xe8WFxcXnDp1SrCdm8vLy/Hmm2/iyJEjsLS0xL/+9S8sXbpUUYDfvXsXTk5OevEQ83nGN8FERESkRN09uo+ztrbGzz//DDc3N6Xx3377DT4+PigqKhI0r0+fPpg/fz5effVVpfHo6GisXLmS5wQ/J3x9ff/0mkgkQmxsrAZnQ7osJCQE165dw8KFC/HVV18hLy8PYrEYX3/9NeRyOYKDg+Hp6YnPPvtMkLzw8HD8+OOP+PDDD1FaWooPPvgA3bt3R3R0NIyNjXHnzh20b98e9fX1guTRs2ERTEREpMNKSkqwfft2ZGRkAAC6deuGyZMnqywffl6ZmZkhISEB7u7uSuPp6eno27cvqqurBc0zNzdHWlqayi6t169fh4eHByoqKgTNIyLt6tChA6Kjo/HSSy+huLgYNjY2OHHihOLYudjYWEybNg3Xrl0TJM/BwQG7d+/G4MGDAQCFhYUYNWoUrKyscPjwYZSWlqJDhw58E6xl3E6PiIhIR50+fRqOjo7YuHEjSkpKUFJSgo0bN8LJyQmnT59WS2ZZWRmKi4tVxouLi/9yo65n0adPH2zdulVlfMuWLejZs6fgea1atcKdO3dUxvPz82FoyC4xIn1TUlKCjh07Ani48sTU1FRpJ+jOnTsjPz9fsLy7d+8q3d/GxgY//fQTKioq4OfnJ/iDPXo2/G1PRESko2bOnInx48dj8+bNin4yuVyO0NBQzJw5E+np6YJnarpH94MPPsDQoUMhk8kUb2ZOnjyJpKQkxMTECJoFAMOHD8eiRYtw6NAhSCQSAEBpaSkWL16MYcOGCZ5HRNollUqRn5+vOJ7o7bffVlpJU1JSAjMzM8Hy7O3tkZGRAScnJ8WYhYUFYmJiMHz4cIwdO1awLHp2XA5NRESko1q3bo3U1FS4uroqjV+5cgWenp64d++e4Jma7tEFgNTUVKxevRqpqamKc4kXLVoEFxcXwbNu3ryJgQMHoqioCF5eXop8Ozs7nDhxgud4EumZMWPGYMiQIQgPD2/0+qZNmxAdHY2TJ08KkhcWFob8/Hx8++23KtcqKiowbNgwJCUlcTm0lvFNMBERkY568cUXkZGRoVIEZ2RkoEePHmrJrKmpQV1dncr4gwcP1FJ0A4Cnpye++uortdz7jzp27Ii0tDR89dVXkMlkaN26NSZPnowJEyao5ZgUItKuQ4cOPfF67969MWjQIMHyli9fjlu3bjV6zcLCAidOnEBycrJgefRs+CaYiIhIR33zzTeYP38+Zs2ahZdeegkAkJCQgE2bNmHFihVKb2s9PDwEyfT19UX37t3x6aefKo3PnDkTaWlpOHPmTLMzysvLFef//lWfMc8JJiIiobEIJiIi0lEGBk/ev1IkEqGhoQEikUiwpXU///wzhg4dit69ezfaoztgwIBmZ4jFYuTn50MqlcLAwAAikUjlM0L/XEREj1haWiI1NVVll3hNuHjxIqqrqzFw4ECNZ9P/cDk0ERGRjrp+/brGM318fHD+/HmsXr0aBw4cUPTobt++XbAe3djYWMXGNHFxcYLcsykMDAzg5uaGX375RTHm5uaGzMxMFt1ELYA23wG+/vrr/F2jA/gmmIiIiFqUXbt2wcrKCoGBgYqxH374AWVlZZg0aZL2JkZEGmFhYQGZTKaVN8G3bt3CgwcPlI5RIs1jEUxERKTDrl27hg0bNiAjIwMA0K1bN4SHh6NTp06CZWi7R7ekpATbt29X+hknT56sdIwJEZFQZsyYgffffx82NjbangppCYtgIiIiHXX8+HEEBATA09MTPj4+AB727MpkMhw5ckSwc2212aN7+vRp+Pv7QyKRoFevXgCAS5cuobS0FEeOHBG8b66srAxyuVylwC4uLoahoSE34iIiQcjlcsX57gCQmJiImpoaeHt7cyd6HcAimIiISEd5eXlhxIgRWLFihdL4woULERMTI9gxG/Hx8fDx8YGhoSHi4+Of+FkhjxIBAHd3d3h7e2Pz5s2KPxjlcjlCQ0Nx7tw5pKenC5r3yiuvwN/fH6GhoUrjW7ZsweHDh3H06FFB84hId1RVVSE+Ph65ubmora1VuhYWFiZIRn5+PoKCgpCQkAAfHx/88MMPeP311xW/W1xcXHDq1Cm0b99ekDx6NiyCiYiIdJSJiQnS09NVNqTKzMyEh4cH7t+/r6WZCad169ZITU1VOQv5ypUr8PT0FPxsYmtra/z8889Kx0sBwG+//QYfHx8UFRUJmkdEuiElJQV+fn6orq5GVVUVrK2tUVhYCFNTU0ilUmRnZwuSExISgmvXrmHhwoX46quvkJeXB7FYjK+//hpyuRzBwcHw9PTEZ599JkgePRvuDk1ERKSjbG1tkZqaqlIEp6amQiqVqi1Xkz26L774IjIyMlSK4IyMDPTo0UPwvJqaGtTV1amMP3jwQPCCm4h0R0REBPz9/bFlyxZIJBIkJCTAyMgIEydORHh4uGA5P/30E6Kjo/HSSy/Bx8cHNjY2OHHiBDp27AgAiIqKwrRp0wTLo2fDIpiIiEhHTZs2DW+99Rays7PRr18/AA97gleuXInZs2erJbOxHt2NGzciKipKLT26YWFhCA8Px9WrV/HSSy8BABISErBp0yasWLECaWlpis96eHg0O69Pnz7YunUrPv30U6XxLVu2oGfPns2+PxHpptTUVHzxxRcwMDCAWCxGTU0NnJ2dsWrVKkyaNAnjxo0TJKekpERR8FpbW8PU1FRpJ+jOnTsjPz9fkCx6diyCiYiIdNR7770HCwsLrF27FosWLQIAdOjQAcuWLROsf+2PZs6cifHjxzfaoztz5kzBe3QnTJgAAJg/f36j10QikaCbcn3wwQcYOnQoZDIZXn75ZQDAyZMnkZSUhJiYmGbfn4h0k5GREQwMDAAAUqkUubm5cHNzg0QiQV5enmA5UqkU+fn5+Pvf/w4AePvtt5VW0ZSUlMDMzEywPHo2LIKJiIh0UF1dHfbt24fg4GBERESgoqICwMPzLdXp6tWrOHjwoNKupmKxGLNnz8aePXsEz7t+/brg93wSHx8fnD9/HqtXr8aBAwfQunVreHh4YPv27SrLzolIf3h5eSEpKQkuLi4YNGgQIiMjUVhYiL1796J79+6C5Xh6euL8+fPo06cPAKhsbHj27FlBVrVQ83BjLCIiIh1lamqKjIwMpaV06ubj44N58+YhMDBQafyHH37AihUrkJCQoLG5EBEJ5eLFi6ioqICvry8KCgoQEhKCc+fOwcXFBTt27FDLHgSNuXDhAkxNTQUtvKnpWAQTERHpqMGDB+Odd95RKUjV6ZtvvsH8+fMxa9asRnt0H99VWai3GdeuXcOGDRuUNuIKDw9Hp06dBLl/eXm54vzf8vLyJ36W5wQTEek/FsFEREQ66sCBA1i0aBEiIiLQs2dPlT4ydSype9Qz92eE7tE9fvw4AgIC4OnpCR8fHwAPN/+SyWQ4cuQIhg0b1uwMsViM/Px8SKVSGBgYQCQSqXxGyJ+JiOhxlpaWSE1NhbOzs7anQv+HRTAREZGOaqwgFboI/aOcnJyn/qwQy7S9vLwwYsQIlb65hQsXIiYmBsnJyc3OiI+Ph4+PDwwNDREfH//Ezw4aNKjZeUSke4qKihAZGYm4uDgUFBSgvr5e6XpxcbHasi0sLCCTyVgE6xAWwURERDrqrwpSTfYKq4uJiQnS09NVNqXKzMyEh4cH7t+/r6WZEZE+8fPzw9WrVzF16lTY2dmprAiZNGmS2rJZBOse7g5NRESko7RV5Kq7R/dxtra2SE1NVSmCU1NTIZVKBc8DHh5Rsn37dqWfb/LkyUrHmBCRfjlz5gzOnj2rsQ2wHjdx4kTuN6BjWAQTERHpsKysrD9dvhcZGSl43p/16L7wwguC9eg+btq0aXjrrbeQnZ2Nfv36KfJWrlyJ2bNnC5oFAKdPn4a/vz8kEgl69eoFANi4cSOioqJw5MgRDBw4UPBMItK+rl274t69e1rJ3rx5s1Zy6c9xOTQREZGO+vLLLzFjxgzY2NigXbt2Ssv3RCKRIP2yf6SJHt3HNTQ0YMOGDVi7di1u3boFAOjQoQPmzZuHsLCwRjexag53d3d4e3tj8+bNirOQ5XI5QkNDce7cOaSnpwuaR0S6ISkpCQsXLkRkZCS6d+8OIyMjpevqeFNbVVWF+Ph45Obmora2VulaWFiY4Hn09FgEExER6SgHBweEhoZiwYIFGsvUZI9uXV0d9u3bhxEjRsDOzg4VFRUAHvbPqUvr1q2RmpoKV1dXpfErV67A09NTa2+KiEi9srKyEBwcrPIgT10bDaakpMDPzw/V1dWoqqqCtbU1CgsLYWpqCqlUiuzsbEHzqGm4HJqIiEhHlZSUICgoSKOZmuzRNTQ0xPTp0xW9ueosfh958cUXkZGRoVIEZ2RkaKVXkIg047XXXoORkRH27dvX6MZYQouIiIC/vz+2bNkCiUSChIQEGBkZYeLEiQgPD1drNv01FsFEREQ6KigoCDExMZg+fbrGMjXdo9unTx+kpKRobBOwsLAwhIeH4+rVq3jppZcAAAkJCdi0aRNWrFiBtLQ0xWfVcQ4zEWnH5cuXkZKSovIATF1SU1PxxRdfwMDAAGKxGDU1NXB2dsaqVaswadIkjBs3TiPzoMaxCCYiItIhGzduVHzduXNnvPfee0hISIC7u7tKD5s6esree+89WFhYYO3atVi0aBGAhz26y5YtU0teaGgo5syZgxs3bqBnz54wMzNTui50ITphwgQAwPz58xu9pu5zmIlIO3r16oW8vDyNFcFGRkaKs96lUilyc3Ph5uYGiUSCvLw8jcyB/hx7gomIiHSIk5PTU31OJBIJ3lOmjR7dR38kPk6dhehfnb38OH04h5mIHvr222+xbNkyzJs3r9GHikI/cBs+fDjeeOMNBAcHY9q0aUhLS0NYWBj27t2LkpISJCYmCppHTcMimIiIiBRMTU2RkZGhsQLwr4pSFqJEJARNP3C7ePEiKioq4Ovri4KCAoSEhODcuXNwcXHBjh07uAeBlrEIJiIi0lFRUVGYO3cuTE1Nlcbv3buH1atXq+Wc4MGDB+Odd95BYGCg4PfWFdeuXcOGDRsUG3J169YN4eHh6NSpk5ZnRkTqwgdu9DgWwURERDpKLBYjPz9fZVfmoqIiSKVStfSsHjhwAIsWLUJERIRGenSBh0eXxMXFoaCgAPX19UrXhC70jx8/joCAAHh6esLHxwfAw42/ZDIZjhw5gmHDhgmaR0REuodFMBERkY4yMDDAnTt3YGtrqzQeGxuL8ePH4+7du2rJ/CN1Lhn88ssvMWPGDNjY2KBdu3ZKx5aIRCKVMz2by8vLCyNGjMCKFSuUxhcuXIiYmBjB84hIt/z666/Izc1FbW2t0nhAQICgOUVFRYiMjPzTB3zFxcWC5lHTsAgmIiLSMW3atIFIJEJZWRksLS2VCkO5XI7KykpMnz4dmzZtEjxb00sGHRwcEBoaigULFgh63z9jYmKC9PR0lXOQMzMz4eHhgfv372tkHkSkWdnZ2Rg7dizS09MVD/YAKH6/Cv2Az8/PD1evXsXUqVMbPZd40qRJguZR0/CIJCIiIh2zYcMGNDQ0YMqUKVi+fDkkEonimrGxMRwdHeHt7a2WbE33xZWUlCAoKEhjeba2tkhNTVUpglNTU1WWnROR/ggPD4eTkxNOnjwJJycnXLhwAUVFRZgzZw7WrFkjeN6ZM2dw9uxZboClo1gEExER6ZhHbwicnJzQr18/laM81E2TPbpBQUGIiYnB9OnTBb3vn5k2bRreeustZGdno1+/fgAe9gSvXLkSs2fP1sgciEjzzp8/j9jYWNjY2MDAwAAGBgbo378/Pv74Y4SFhSElJUXQvK5du+LevXuC3pOEw+XQREREOqy+vh5Xr15ttCAdOHCg4Hma6NHduHGj4uuqqiqsW7cOo0aNavTszrCwsGbnPa6hoQEbNmzA2rVrcevWLQBAhw4dMG/ePISFhaksWSQi/dCmTRskJyfDyckJnTp1wrZt2+Dr64tr167B3d0d1dXVguYlJSVh4cKFiIyMRPfu3VV+t1laWgqaR03DIpiIiEhHJSQkIDg4GDk5Ofjj/1yrY5MqQDM9uk5OTk/1OZFIhOzsbMFy6+rqsG/fPowYMQJ2dnaoqKgAAFhYWAiWQUS6acCAAZgzZw4CAwMRHByMkpISLFmyBFu3bsWlS5dw+fJlQfOysrIQHBys8uBQXZsMUtOwCCYiItJRnp6e6NKlC5YvX4727durvKV8vFdYKJaWlkhNTYWzs7Pg99YFpqamyMjI4JmgRC3M8ePHUVVVhXHjxuHq1asYPXo0MjMz0bZtW3zzzTcYMmSIoHl9+vSBoaEhwsPDG90Ya9CgQYLmUdOwCCYiItJRZmZmkMlk6Ny5s8Yyp06dit69e2usRzcqKgpz586Fqamp0vi9e/ewevVqwXuQBw8ejHfeeQeBgYGC3peInj/FxcWK3fiFZmpqipSUFLi6ugp+b2o+FsFEREQ6asiQIZg/fz5Gjhyp1hxt9uiKxWLk5+er7MxcVFQEqVQq+JLBAwcOYNGiRYiIiEDPnj1hZmamdN3Dw0PQPCJqmQYOHIjIyEgMHTpU21OhRrAIJiIi0lHff/89lixZgnnz5jVakApVsGmrRxcADAwMcOfOHdja2iqNx8bGYvz48bh7967geX/06MxQ9ukR6Zdx48Y99Wejo6MFzf7222+xbNkytf/+pmfDIpiIiEhHNVawPfK8F2yPliCWlZXB0tJSaTmiXC5HZWUlpk+fjk2bNgmam5OT88Tr7BUm0h+TJ09+6s/u3LlT0Gw+cNNtLIKJiIh0lDYKNk316O7evRsNDQ2YMmUKNmzYoLTJl7GxMRwdHeHt7S1IFhGRpvGBm25jEUxERKTjfv31V+Tm5qK2tlYxJhKJ4O/vL3iWpnt04+Pj0a9fP5WlguqUlZWFuLi4Rs9eFnojLiLSbWlpaejVq5fS71fSf4bangARERE1Ljs7G2PHjkV6erpiGR0AxdJhdSyne7RU749kMhmsra0Fzxs0aBDq6+uRmZnZaFE6cOBAQfO+/PJLzJgxAzY2NmjXrp3SzyoSiVgEE7UwDQ0NqKurU9v9G3uICQABAQFqy6S/xiKYiIhIR4WHh8PJyQknT56Ek5MTEhMTUVxcjDlz5mDNmjWCZj3q0RWJROjSpcuf9ugKLSEhAcHBwcjJycEfF6epo2/ugw8+wIcffogFCxYIel8ien6p44gkbTzEpKfHIpiIiEhHnT9/HrGxsbCxsYGBgQHEYjH69++Pjz/+GGFhYUhJSREsa8OGDYoe3eXLl2usR3f69Ono1asX/vvf/6J9+/Zq+WP0cSUlJQgKClJrBhHRHx9iXrhwAUVFRWp5iElNxyKYiIhIR8nlclhYWAAAbGxscOvWLbi6usLBwQFXrlwRNGvSpEkAHh6XpMke3aysLBw8eBCdO3fWSF5QUBBiYmLU8labiHRPeXn5E69XVFSoJfePDzENDAzU9hCTmo5FMBERkY7q3r07ZDIZnJyc0LdvX6xatQrGxsbYunUrnJ2d1ZKp6R7dvn374urVq2otgjdu3Kj4unPnznjvvfeQkJDQ6NmdYWFhapsHEWmelZXVE1eY/Nk+CM2lyYeY1HQsgomIiHTUkiVLUFVVBeDh0UWjR4/GgAED0LZtW3zzzTdqydR0j+6sWbMwZ84c3L59u9Gi1MPDo9kZ69evV/re3Nwc8fHxiI+PVxoXiUQsgon0TFxcnFZytfEQk54ej0giIiJ6jhQXFys2sVIHT09PdOnSBcuXL2+0R/fxXmEhGBgY/Ok1dRTdRNSyxMbGYtCgQRCLxRrNPX78OKqqqjBu3DhcvXoVo0ePRmZmpuIh5pAhQzQ6H1LGIpiIiIgUzMzMIJPJNNajm5OT88TrDg4OguZFRUVh7ty5MDU1VRq/d+8eVq9ezSOSiPSMs7MzSktLMXLkSIwZMwavvPIKLC0ttTIXdT/EpKfHIpiIiIgUhgwZgvnz52PkyJEazW3sLE2RSAR/f39Bc8RiMfLz8yGVSpXGi4qKIJVK+eaZSA+lpaXh8OHDOHz4MNLT09G/f38EBARgzJgxsLe31/b0SAtYBBMREZHC999/jyVLlmDevHlq69F9nKbP0jQwMMCdO3dga2urNB4bG4vx48fj7t27guYRkW65deuWoiCOi4uDq6srAgICEBAQgF69ejXr3uPGjXvqz0ZHRzcri5qHRTAREREpaLpH19/fH2KxGNu2bYOTkxMSExNRXFysOEtzwIABguQ8WoJYVlYGS0tLpeWIcrkclZWVmD59OjZt2iRIHhHpvqqqKhw7dgyHDx/G0aNHMXv2bCxevPiZ7zd58uSn/uzOnTufOYeaj0UwERERKWi6R9fGxgaxsbHw8PCARCLBhQsX4OrqitjYWMyZM0ewszR3796NhoYGTJkyBRs2bFDa4MvY2BiOjo7w9vYWJIuInj9yuRzFxcUqq0RIP/GIJCIiIlJ4VOT+WY+u0EWwps7SnDRpEgDAyckJ/fr1U1nmTUT6r6qqCvHx8Y3+bps1a5ZGCuC0tDT06tVLKZ80j0UwERERKWi6R1fTZ2kOGjQI9fX1yMzMREFBAerr65WuDxw4UPBMItK+lJQU+Pn5obq6GlVVVbC2tkZhYSFMTU0hlUoxa9YsjcyjoaEBdXV1GsmiP8cimIiIiBTCw8Ph5OSEkydPNtqjK7QlS5agqqoKwMPji0aPHo0BAwYoztIUWkJCAoKDg5GTk4M/doTxXGIi/RUREQF/f39s2bIFEokECQkJMDIywsSJExEeHq7RufCIJO1jTzAREREpaKpH90nUeZamp6cnunTpguXLl6N9+/YqGY/3ChOR/rCyskJiYiJcXV1hZWWF8+fPw83NDYmJiZg0aRJ+++03jcxDJpPhxRdf5AM3LeObYCIiIlLQVI/uk1hbW6vt3llZWTh48CA6d+6stgwi0j1GRkaK3e+lUilyc3Ph5uYGiUSCvLw8wXLKy8ufeL2iokKwLHp2LIKJiIhIQdM9uprWt29fXL16lUUwUQvj5eWFpKQkuLi4YNCgQYiMjERhYSH27t2L7t27C5ZjZWX1xFUsDQ0NXA6tA1gEExERkYKme3Q1bdasWZgzZw5u374Nd3d3lV2iPTw8tDQzIlKnjz76SPEW9sMPP0RISAhmzJgBFxcX7NixQ7CcuLg4we5F6sOeYCIiInoidfboatqj5ZCN4cZYRNRcsbGxGDRoEMRisbanQk/AIpiIiIhajJycnCdeF/ocZCJqWZydnVFaWoqRI0dizJgxeOWVV2BpaantadEfsAgmIiKiFufXX39Fbm4uamtrFWMikQj+/v5anBURqUtRUREiIyMRFxfX6BnhxcXFgmWlpaXh8OHDOHz4MNLT09G/f38EBARgzJgxsLe3FyyHnh2LYCIiImoxsrOzMXbsWKSnp0MkEinOCn601JvLoYn0k5+fH65evYqpU6fCzs5Opb1j0qRJasm9deuWoiCOi4uDq6srAgICEBAQgF69eqklk/4ai2AiIiJqMfz9/SEWi7Ft2zY4OTkhMTERxcXFmDNnDtasWYMBAwZoe4pEpAYWFhY4e/YsevToobU5VFVV4dixYzh8+DCOHj2K2bNnY/HixVqbT0vGIpiIiIhaDBsbG8TGxsLDwwMSiQQXLlyAq6srYmNjMWfOHKSkpGh7ikSkBr1798ann36Kl156SdtTAfBw1UlxcTFsbW21PZUWiUckERERUYshl8thYWEB4GFBfOvWLbi6usLBwQFXrlzR8uyISF0+//xzLFy4EJGRkejevbvK8Wjq2LyqqqoK8fHxje4/MGvWLBbAWsQimIiIiFqM7t27QyaTwcnJCX379sWqVatgbGyMrVu3wtnZWdvTIyI1sbKyQnl5OYYMGaI03tDQoJbj0VJSUuDn54fq6mpUVVXB2toahYWFMDU1hVQqxaxZswTNo6ZhEUxEREQtxpIlS1BVVQUAiIqKwujRozFgwAC0bdsW33zzjZZnR0Tq8tprr8HIyAj79u1rdGMsoUVERMDf3x9btmyBRCJBQkICjIyMMHHiRISHh6s1m/4ae4KJiIioRSsuLkabNm3U/kcxEWmPqakpUlJS4OrqqpE8KysrJCYmwtXVFVZWVjh//jzc3NyQmJiISZMm4bffftPIPKhxBtqeABEREZE2WVtbswAm0nO9evVCXl6exvKMjIxgYPCw1JJKpcjNzQUASCQSjc6DGsfl0EREREREpNdmzZqF8PBwzJs3D+7u7iobY3l4eAia5+XlhaSkJLi4uGDQoEGIjIxEYWEh9u7di+7duwuaRU3H5dBERERERKTXHr2VfZxIJFLbxlgXL15ERUUFfH19UVBQgJCQEJw7dw4uLi7YsWOHVs8rJhbBRERERESk53Jycp543cHBQUMzIV3AIpiIiIiIiIhaDPYEExERERFRi/Drr78iNzcXtbW1SuMBAQGC5hQVFSEyMhJxcXEoKChAfX290vXi4mJB86hpWAQTEREREZFey87OxtixY5Genq7oBQag2Ble6J7g119/HVevXsXUqVM1ci4xNQ2XQxMRERERkV7z9/eHWCzGtm3b4OTkhAsXLqCoqAhz5szBmjVrMGDAAEHzLCwscPbsWW6ApaP4JpiIiIiIiPTa+fPnERsbCxsbGxgYGMDAwAD9+/fHxx9/jLCwMKSkpAia17VrV9y7d0/Qe5JwVPcKJyIiIiIi0iNyuRwWFhYAABsbG9y6dQvAw12hr1y5Inje559/jnfffRfx8fEoKipCeXm50j/SLr4JJiIiIiIivda9e3fIZDI4OTmhb9++WLVqFYyNjbF161Y4OzsLnmdlZYXy8nIMGTJEaVxd5xJT07AIJiIiIiIivbZkyRJUVVUBAKKiojB69GgMGDAAbdu2xTfffCN43muvvQYjIyPs27ePG2PpIG6MRURERERELU5xcTHatGmjlgLV1NQUKSkpcHV1Ffze1HzsCSYiIiIiohbH2tpabW9oe/Xqhby8PLXcm5qPb4KJiIiIiEjvjBs37qk/Gx0dLWj2t99+i2XLlmHevHlwd3eHkZGR0nUPDw9B86hpWAQTEREREZHemTx58lN/dufOnYJmGxioLrgViUTcGEtHsAgmIiIiIiISUE5OzhOvOzg4aGgm1BgWwURERERERNRi8IgkIiIiIiLSO15eXk+98VVycrJa5vDrr78iNzcXtbW1SuMBAQFqyaOnwyKYiIiIiIj0TmBgoNays7OzMXbsWKSnpyt6gQEoinL2BGsXl0MTEREREREJyN/fH2KxGNu2bYOTkxMuXLiAoqIizJkzB2vWrMGAAQO0PcUWjecEExERERFRixEaGorCwkK1Zpw/fx5RUVGwsbGBgYEBDAwM0L9/f3z88ccICwtTazb9NRbBRERERETUYvz73/9GeXm5WjPkcjksLCwAADY2Nrh16xaAh7tCX7lyRa3Z9NfYE0xERERERC2GJrpBu3fvDplMBicnJ/Tt2xerVq2CsbExtm7dCmdnZ7Xn05OxCCYiIiIiIhLQkiVLUFVVBQCIiorC6NGjMWDAALRt2xbffPONlmdH3BiLiIiIiIhIzYqLi9GmTZunPraJ1IdFMBERERERtRj3799XObfX0tJSS7MhbeByaCIiIiIi0mvV1dWYP38+Dhw4gKKiIpXrQpzbO27cuKf+bHR0dLPz6NmxCCYiIiIiIr02b948xMXFYfPmzXj99dexadMm3Lx5E1988QVWrFghSIZEIhHkPqR+XA5NRERERER6zd7eHnv27MHgwYNhaWmJ5ORkdO7cGXv37sXXX3+No0ePanuKpEE8J5iIiIiIiPRacXGx4mgiS0tLFBcXAwD69++P06dPa3NqpAVcDk1ERERERHrN2dkZ169fh729Pbp27YoDBw6gT58+OHLkCKysrATJ8PLyeuqdn5OTkwXJpGfDIpiIiIiIiPTa5MmTIZPJMGjQICxcuBD+/v747LPP8ODBA6xbt06QjMDAQEHuQ+rHnmAiIiIiImpRcnJycOnSJXTu3BkeHh7ang5pGHuCiYiIiIhIr+3Zswc1NTWK7x0cHDBu3Dh07doVe/bsUWt2aGgoCgsL1ZpBTcM3wUREREREpNfEYjHy8/MhlUqVxouKiiCVSgU5J/jPWFpaIjU1VbExF2kf3wQTEREREZFea2hoaHTTqhs3bqj9fF++c9Q93BiLiIiIiIj00qMdm0UiEV5++WUYGv6v/JHL5bh+/TpGjhypxRmSNrAIJiIiIiIivfRox+bU1FSMGDEC5ubmimvGxsZwdHTEP/7xD7XOoaKiQq33p6ZjTzAREREREem13bt3Y/z48TAxMdF49v3791FbW6s0ZmlpqfF50P+wCCYiIiIiIr1XWlqKgwcP4tq1a5g3bx6sra2RnJwMOzs7dOzYUdCs6upqzJ8/HwcOHEBRUZHKdXVuxEV/jRtjERERERGRXktLS0OXLl2wcuVKrFmzBqWlpQCA6OhoLFq0SPC8efPmITY2Fps3b0arVq2wbds2LF++HB06dFD7kUz01/gmmIiIiIiI9NrLL7+Mnj17YtWqVbCwsIBMJoOzszPOnTuH4OBg/P7774Lm2dvbY8+ePRg8eDAsLS2RnJyMzp07Y+/evfj6669x9OhRQfOoafgmmIiIiIiI9NrFixfxr3/9S2W8Y8eOuH37tuB5xcXFinOBLS0tUVxcDADo378/Tp8+LXgeNQ2LYCIiIiIi0mutWrVCeXm5ynhmZiZsbW0Fz3N2dsb169cBAF27dsWBAwcAAEeOHIGVlZXgedQ0LIKJiIiIiEivBQQEICoqCg8ePAAAiEQi5ObmYsGCBWo5Imny5MmQyWQAgIULF2LTpk0wMTFBREQE5s2bJ3geNQ17gomIiIiISK+VlZXh1VdfxcWLF1FRUYEOHTrg9u3b8Pb2xtGjR2FmZqbW/JycHFy6dAmdO3eGh4eHWrPor7EIJiIiIiKiFuHs2bNIS0tDZWUlXnzxRQwdOlQtOXv27MH48ePRqlUrpfHa2lrs378fISEhasmlp8MimIiIiIiISEBisRj5+fmQSqVK40VFRZBKpTwnWMsMtT0BIiIiIiIidUtKSkJcXBwKCgpQX1+vdG3dunWCZjU0NEAkEqmM37hxAxKJRNAsajoWwUREREREpNc++ugjLFmyBK6urrCzs1MqUBsrVp+Vl5cXRCIRRCIRXn75ZRga/q/cksvluH79OkaOHClYHj0bFsFERERERKTXPvnkE+zYsQNvvPGGWnMCAwMBAKmpqRgxYgTMzc0V14yNjeHo6KiW3aipaVgEExERERGRXjMwMICPj4/ac5YuXQoAcHR0xPjx42FiYqL2TGo6boxFRERERER6bdWqVbh16xY2bNigsczS0lIcPHgQ165dw7x582BtbY3k5GTY2dmhY8eOGpsHqWIRTEREREREeq2+vh6jRo1CZmYmunXrBiMjI6Xr0dHRgualpaVh6NChkEgk+P3333HlyhU4OztjyZIlyM3NxZ49ewTNo6Yx0PYEiIiIiIiI1CksLAxxcXHo0qUL2rZtC4lEovRPaBEREXjjjTeQlZWltCTaz88Pp0+fFjyPmoZvgomIiIiISK9ZWFhg//79GDVqlEbyJBIJkpOT0alTJ1hYWEAmk8HZ2Rk5OTlwdXXF/fv3NTIPahzfBBMRERERkV6ztrZGp06dNJbXqlUrlJeXq4xnZmbC1tZWY/OgxrEIJiIiIiIivbZs2TIsXboU1dXVGskLCAhAVFQUHjx4AODhWcS5ublYsGABj0jSAVwOTUREREREes3LywvXrl1DQ0MDHB0dVTbGSk5OFjSvrKwMr776Ki5evIiKigp06NABt2/fhre3N44ePQozMzNB86hpeE4wERERERHptcDAQI3mSSQSnDhxAmfPnkVaWhoqKyvx4osvYujQoRqdBzWOb4KJiIiIiIioxeCbYCIiIiIiIoElJSUhLi4OBQUFqK+vV7q2bt06Lc2KABbBRERERETUAkyZMgXt27fHhx9+qBhbvHgxbt++jR07dgia9dFHH2HJkiVwdXWFnZ0dRCKR4trjX5N2sAgmIiIiIiK9d/36dZU3sjdv3kReXp7gWZ988gl27NiBN954Q/B7U/OxJ5iIiIiIiEhA7du3x+nTp+Hi4qLtqVAjeE4wERERERHprQcPHqBTp07IyMjQWGZERAQ2bdqksTxqGi6HJiIiIiIivWVkZIT79+9rNHPu3LkYNWoUOnXqhG7duqmcSxwdHa3R+ZAyvgkmIiIiIiK9NnPmTKxcuRJ1dXUayQsLC0NcXBy6dOmCtm3bQiKRKP0j7WJPMBERERER6bWxY8fi5MmTMDc3h7u7O8zMzJSuC/1m1sLCAvv378eoUaMEvS8Jg8uhiYiIiIhIr1lZWeEf//iHxvKsra3RqVMnjeVR0/BNMBERERERkYB27tyJH3/8ETt37oSpqam2p0N/wCKYiIiIiIj0Xl1dHU6dOoVr164hODgYFhYWuHXrFiwtLWFubi5olpeXF65du4aGhgY4OjqqbIyVnJwsaB41DZdDExERERGRXsvJycHIkSORm5uLmpoaDBs2DBYWFli5ciVqamqwZcsWQfMCAwMFvR8Ji2+CiYiIiIhIrwUGBsLCwgLbt29H27ZtIZPJ4OzsjFOnTmHatGnIysrS9hRJg/gmmIiIiIiI9NqZM2dw7tw5GBsbK407Ojri5s2bWpoVaQvPCSYiIiIiIr1WX18PuVyuMn7jxg1YWFioJXPKlCl49913lcYWL16MKVOmqCWPnh6LYCIiIiIi0mvDhw/Hhg0bFN+LRCJUVlZi6dKl8PPzU0vm9evXVd4y37x5E7///rta8ujpsSeYiIiIiIj02o0bNzBixAg0NDQgKysLvXr1QlZWFmxsbHD69GlIpVJtT5E0iEUwERERERHpvbq6Ouzfvx9paWmorKzEiy++iNdeew2tW7cWNOfBgwfo2rUr/vOf/8DNzU3Qe5MwuDEWERERERHpPUNDQ0ycOFHtOUZGRrh//77ac+jZ8U0wERERERHpvaysLMTFxaGgoAD19fVK1yIjIwXN+uijj5CZmYlt27bB0JDvHXUNi2AiIiIiItJrX375JWbMmAEbGxu0a9cOIpFIcU0kEiE5OVnQvLFjx+LkyZMwNzeHu7s7zMzMlK5HR0cLmkdNwyKYiIiIiIj0moODA0JDQ7FgwQKN5E2ePPmJ13fu3KmReVDjWAQTEREREZFes7S0RGpqKpydnbU9FdIBPCeYiIiIiIj0WlBQEGJiYjSaWVdXh59++glffPEFKioqAAC3bt1CZWWlRudBqvgmmIiIiIiI9M7GjRsVX1dVVWHdunUYNWoU3N3dYWRkpPTZsLAwQbNzcnIwcuRI5ObmoqamBpmZmXB2dkZ4eDhqamqwZcsWQfOoaVgEExERERGR3nFycnqqz4lEImRnZwuaHRgYCAsLC2zfvh1t27aFTCaDs7MzTp06hWnTpiErK0vQPGoa7tdNRERERER65/r161rLPnPmDM6dOwdjY2OlcUdHR9y8eVNLs6JH2BNMRERERER6LSoqCtXV1Srj9+7dQ1RUlOB59fX1kMvlKuM3btyAhYWF4HnUNFwOTUREREREek0sFiM/Px9SqVRpvKioCFKptNGCtTnGjx8PiUSCrVu3wsLCAmlpabC1tcWYMWNgb2/PI5K0jMuhiYiIiIhIrzU0NEAkEqmMy2QyWFtbC563du1ajBgxAt26dcP9+/cRHByMrKws2NjY4OuvvxY8j5qGb4KJiIiIiEgvtWnTBiKRCGVlZbC0tFQqhOVyOSorKzF9+nRs2rRJ8Oy6ujrs378faWlpqKysxIsvvojXXnsNrVu3FjyLmoZFMBERERER6aXdu3ejoaEBU6ZMwYYNGyCRSBTXjI2N4ejoCG9vby3OkLSBRTAREREREem1+Ph4+Pj4wNBQc92gWVlZiIuLQ0FBAerr65WuRUZGamwepIpFMBERERER6b1r165h586duHbtGj755BNIpVIcO3YM9vb2eOGFFwTN+vLLLzFjxgzY2NigXbt2SsuwRSIRkpOTBc2jpmERTEREREREei0+Ph6vvPIKfHx8cPr0aWRkZMDZ2RkrVqzAxYsXcfDgQUHzHBwcEBoaigULFgh6XxIGzwkmIiIiIiK9tnDhQnzwwQc4ceIEjI2NFeNDhgxBQkKC4HklJSUICgoS/L4kDBbBRERERESk19LT0zF27FiVcalUisLCQsHzgoKCEBMTI/h9SRg8J5iIiIiIiPSalZUV8vPz4eTkpDSekpKCjh07CpKxceNGxdedO3fGe++9h4SEBLi7u8PIyEjps2FhYYJk0rNhTzAREREREem1uXPnIjExEd9++y26dOmC5ORk3LlzByEhIQgJCcHSpUubnfHHAvvPiEQiZGdnNzuPnh2LYCIiIiIi0mu1tbWYOXMmdu3aBblcDkNDQ8jlcgQHB2PXrl0Qi8XaniJpEItgIiIiIiJqEXJzc3H58mVUVlbCy8sLLi4uasmJiorC3LlzYWpqqjR+7949rF69mucEaxmLYCIiIiIiIgGJxWLk5+dDKpUqjRcVFUEqlUIul2tpZgRwYywiIiIiItJzs2fPbnRcJBLBxMQEnTt3xpgxY2BtbS1IXkNDA0Qikcq4TCYTLIOeHd8EExERERGRXvP19UVycjLkcjlcXV0BAJmZmRCLxejatSuuXLkCkUiEs2fPolu3bs+c06ZNG4hEIpSVlcHS0lKpEJbL5aisrMT06dOxadOmZv9M9OxYBBMRERERkV7bsGEDzpw5g507d8LS0hIAUFZWhjfffBP9+/fHtGnTEBwcjHv37uH48ePPnLN79240NDRgypQp2LBhAyQSieKasbExHB0d4e3t3eyfh5qHRTAREREREem1jh074sSJEypveX/55RcMHz4cN2/eRHJyMoYPH47CwsJm58XHx8PHxweGhuw+1UUG2p4AERERERGROpWVlaGgoEBl/O7duygvLwcAWFlZoba2VpC8QYMGIScnB0uWLMGECRMU2ceOHcMvv/wiSAY9OxbBRERERESk18aMGYMpU6bg+++/x40bN3Djxg18//33mDp1KgIDAwEAFy5cQJcuXQTJi4+Ph7u7OxITExEdHY3KykoADzfGWrp0qSAZ9Oy4HJqIiIiIiPRaZWUlIiIisGfPHtTV1QEADA0NMWnSJKxfvx5mZmZITU0FAHh6ejY7z9vbG0FBQZg9ezYsLCwgk8ng7OyMCxcuYNy4cbhx40azM+jZsQgmIiIiIqIWobKyEtnZ2QAAZ2dnmJubqyXH3Nwc6enpcHJyUiqCf//9d3Tt2hX3799XSy49HS6HJiIiIiKiFsHc3BzW1tawtrZWWwEMPOwvzs/PVxlPSUlBx44d1ZZLT4dFMBERERER6bX6+npERUVBIpHAwcEBDg4OsLKywvvvv4/6+nrB8/75z39iwYIFuH37NkQiEerr6/Hzzz9j7ty5CAkJETyPmobLoYmIiIiISK8tWrQI27dvx/Lly+Hj4wMAOHv2LJYtW4Zp06bhww8/FDSvtrYWM2fOxK5duyCXy2FoaAi5XI7g4GDs2rULYrFY0DxqGhbBRERERESk1zp06IAtW7YgICBAafzQoUMIDQ3FzZs31ZKbm5uLy5cvo7KyEl5eXnBxcVFLDjUNT28mIiIiIiK9VlxcjK5du6qMd+3aFcXFxWrLtbe3h729vdruT8+GRTAREREREem1Hj164LPPPsPGjRuVxj/77DP06NFD8LzZs2c3Oi4SiWBiYoLOnTtjzJgxsLa2Fjyb/hqXQxMRERERkV6Lj4/HqFGjYG9vD29vbwDA+fPnkZeXh6NHj2LAgAGC5vn6+iI5ORlyuRyurq4AgMzMTIjFYnTt2hVXrlyBSCTC2bNn0a1bN0Gz6a9xd2giIiIiItJrTk5OyMzMxNixY1FaWorS0lKMGzcOV65cgYODg+B5Y8aMwdChQ3Hr1i1cunQJly5dwo0bNzBs2DBMmDABN2/exMCBAxERESF4Nv01vgkmIiIiIiK9JhaLkZ+fD6lUqjReVFQEqVQKuVwuaF7Hjh1x4sQJlbe8v/zyC4YPH46bN28iOTkZw4cPR2FhoaDZ9Nf4JpiIiIiIiPTan733q6yshImJieB5ZWVlKCgoUBm/e/cuysvLAQBWVlaora0VPJv+GjfGIiIiIiIivfRogyqRSITIyEiYmpoqrsnlciQmJsLT01Pw3DFjxmDKlClYu3YtevfuDQBISkrC3LlzERgYCAC4cOECunTpIng2/TUuhyYiIiIiIr3k6+sL4OHGWN7e3jA2NlZcMzY2hqOjI+bOnSv4+b2VlZWIiIjAnj17UFdXBwAwNDTEpEmTsH79epiZmSE1NRUA1FKE05OxCCYiIiIiIr02efJkfPLJJ7C0tNRobmVlJbKzswEAzs7OMDc312g+NY5FMBERERERkZrcuHEDAPC3v/1NyzOhR7gxFhERERERkYDq6+sRFRUFiUQCBwcHODg4wMrKCu+//z7q6+u1Pb0WjxtjERERERERCejdd9/F9u3bsWLFCvj4+AAAzp49i2XLluH+/fv48MMPtTzDlo3LoYmIiIiIiATUoUMHbNmyBQEBAUrjhw4dQmhoKG7evKmlmRHA5dBERERERESCKi4uRteuXVXGu3btiuLiYi3MiB7HIpiIiIiIiEhAPXr0wGeffaYy/tlnn6FHjx5amBE9jsuhiYiIiIiIBBQfH49Ro0bB3t4e3t7eAIDz588jLy8PR48exYABA7Q8w5aNb4KJiIiIiIgE5OTkhMzMTIwdOxalpaUoLS3FuHHjcOXKFTg4OGh7ei0e3wQTEREREREJSCwWIz8/H1KpVGm8qKgIUqkUcrlcSzMjgG+CiYiIiIiIBPVn7xkrKythYmKi4dnQH/GcYCIiIiIiIgHMnj0bACASiRAZGQlTU1PFNblcjsTERHh6emppdvQIi2AiIiIiIiIBpKSkAHj4Jjg9PR3GxsaKa8bGxujRowfmzp2rrenR/2FPMBERERERkYAmT56MTz75BJaWltqeCjWCRTARERERERG1GNwYi4iIiIiIiFoMFsFERERERETUYrAIJiIiIiIiohaDRTARERERac3gwYPxzjvvPPXnd+3aBSsrK7XNh4j0H4tgIiIiItJL0dHRGDZsGGxtbWFpaQlvb28cP35c29MiIi1jEUxEREREeun06dMYNmwYjh49ikuXLsHX1xf+/v6Ks1yJqGViEUxEREREKgYPHoxZs2bhnXfeQZs2bWBnZ4cvv/wSVVVVmDx5MiwsLNC5c2ccO3ZM8Z+Jj49Hnz590KpVK7Rv3x4LFy5EXV2d4npVVRVCQkJgbm6O9u3bY+3atSq5NTU1mDt3Ljp27AgzMzP07dsXp06deqafYcOGDZg/fz569+4NFxcXfPTRR3BxccGRI0ee6X5EpB9YBBMRERFRo3bv3g0bGxtcuHABs2bNwowZMxAUFIR+/fohOTkZw4cPx+uvv47q6mrcvHkTfn5+6N27N2QyGTZv3ozt27fjgw8+UNxv3rx5iI+Px6FDhxATE4NTp04hOTlZKfPtt9/G+fPnsX//fqSlpSEoKAgjR45EVlZWs3+e+vp6VFRUwNrautn3IqLnl6ihoaFB25MgIiIiIt0yePBgyOVynDlzBgAgl8shkUgwbtw47NmzBwBw+/ZttG/fHufPn8eRI0fw3XffISMjAyKRCADw+eefY8GCBSgrK0N1dTXatm2Lf//73wgKCgIAFBcX429/+xveeustbNiwAbm5uXB2dkZubi46dOigmMvQoUPRp08ffPTRR9i1axfeeecdlJaWNvlnWrVqFVasWIHffvsNUqm0mf8NEdHzylDbEyAiIiIi3eTh4aH4WiwWo23btnB3d1eM2dnZAQAKCgqQkZEBb29vRQEMAD4+PqisrMSNGzdQUlKC2tpa9O3bV3Hd2toarq6uiu/T09Mhl8vRpUsXpXnU1NSgbdu2zfpZ9u3bh+XLl+PQoUMsgIlaOBbBRERERNQoIyMjpe9FIpHS2KOCt76+XpC8yspKiMViXLp0CWKxWOmaubn5M993//79ePPNN/Htt99i6NChzZ0mET3nWAQTERERUbO5ubnhu+++Q0NDg6I4/vnnn2FhYYG//e1vsLa2hpGRERITE2Fvbw8AKCkpQWZmJgYNGgQA8PLyglwuR0FBAQYMGCDIvL7++mtMmTIF+/fvx6hRowS5JxE937gxFhERERE1W2hoKPLy8jBr1iz89ttvOHToEJYuXYrZs2fDwMAA5ubmmDp1KubNm4fY2FhcvnwZb7zxBgwM/vfnaJcuXfDaa68hJCQE0dHRuH79Oi5cuICPP/4Y//3vf5s8p3379iEkJARr165F3759cfv2bdy+fRtlZWVC/uhE9JxhEUxEREREzdaxY0ccPXoUFy5cQI8ePTB9+nRMnToVS5YsUXxm9erVGDBgAPz9/TF06FD0798fPXv2VLrPzp07ERISgjlz5sDV1RWBgYFISkpSvD1uiq1bt6Kurg4zZ85E+/btFf/Cw8Ob/fMS0fOLu0MTERERERFRi8E3wURERERERNRisAgmIiIioufSCy+8AHNz80b/ffXVV9qeHhHpKC6HJiIiIqLnUk5ODh48eNDoNTs7O1hYWGh4RkT0PGARTERERERERC0Gl0MTERERERFRi8EimIiIiIiIiFoMFsFERERERETUYrAIJiIiIiIiohaDRTARERERERG1GCyCiYiIiIiIqMVgEUxEREREREQtBotgIiIiIiIiajH+PwYhxOTUH4BAAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a heatmap\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "sns.heatmap(results_matrix, annot=True, \n",
    "            # cmap from white to blue\n",
    "            cmap=\"Blues\",\n",
    "            )\n",
    "# plt.xticks(rotation=-45)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
